chore: redefined and connected pricers (#29)

* first implementation of elasticity demand computation

* chor: fixing test :(

* feature: rudemantary defintition of pricing pipeline

* chor: fixing cross product missing data

* add warning

* feature: e2e pricing pipeline with inference

.github/workflows/pytest.yml

@@ -0,0 +1,30 @@
+name: Run Tests
+on:
+  push:
+    paths:
+      - 'experiments/**'
+      - 'backend/**'
+      - 'requirements.txt'
+      - '.github/workflows/pytest.yml'
+  pull_request:
+    paths:
+      - 'experiments/**'
+      - 'backend/**'
+      - 'requirements.txt'
+      - '.github/workflows/pytest.yml'
+jobs:
+  test:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - uses: actions/setup-python@v5
+        with:
+          python-version: '3.13'
+          cache: 'pip'
+      - name: Install dependencies
+        run: |
+          python -m venv .venv
+          .venv/bin/pip install --upgrade pip
+          .venv/bin/pip install -r requirements.txt
+      - name: Run tests
+        run: .venv/bin/pytest -v

.gitignore

@@ -1,6 +1,13 @@
 **/.env
 **/.venv
-PHANTOM.wiki/
+**/__pycache__
+**/.ipynb_checkpoints/
 **/.virtual_documents/
-**/__pycache__/
-**/.ipynb_checkpoints/
+**/session_*.svg
+**/*graph.svg
+paper/src/bib/auto
+
+# Airflow logs - exclude DAG run logs
+experiments/airflow/logs/*
+experiments/airflow/logs/scheduler/
+experiments/airflow/logs/dag_processor_manager/

Makefile

@@ -4,6 +4,10 @@ BUILDDIR  := build
 TEX       := main.tex
 JOBNAME   := main
 PDF       := paper/$(BUILDDIR)/$(JOBNAME).pdf
+VENV      := .venv
+PYTHON    := $(VENV)/bin/python
+PIP       := $(VENV)/bin/pip
+PYTEST    := $(VENV)/bin/pytest
 
 .DEFAULT_GOAL := help
 
@@ -35,5 +39,18 @@ clean:
 	$(LATEXMK) -C -jobname=$(JOBNAME) -outdir=../$(BUILDDIR) || true
 	rm -rf paper/$(BUILDDIR)/*
 
+$(VENV):
+	python3 -m venv $(VENV)
+	$(PIP) install --upgrade pip
 
-.PHONY: all pdf clean watch run.webapp
+install: $(VENV)
+	$(PIP) install -r requirements.txt
+
+test: $(VENV)
+	$(PYTEST) -v
+
+count-lines:
+	@find . \( -path '*/node_modules' -o -path '*/.venv' -o -path '*/venv' \) -prune -o \
+	\( -name "*.ts" -o -name "*.py" \) -type f -print0 | xargs -0 cat | wc -l
+
+.PHONY: all pdf clean watch run.webapp install test

backend/provider/app.py

@@ -0,0 +1,182 @@
+from fastapi import FastAPI, HTTPException, Query
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel
+from typing import Literal, Optional
+import uvicorn, os, sys
+from supabase import create_client, Client
+from dotenv import load_dotenv
+import numpy as np
+import pandas as pd
+load_dotenv()
+
+# Local imports of registry and pricing function
+
+sys.path.append(os.path.dirname(os.path.abspath(__file__))+ "/../../experiments/")
+from procesing.providers import SupabaseProvider, BackendAPIProvider
+from procesing.pricers import (
+    StaticPricer,
+    RandomPricer,
+    ElasticityBasedPricer
+)
+from procesing.steps import (
+    StateSpace,
+    PredictPricesStep
+)
+from procesing import PipelineContext
+sys.path.append(os.path.dirname(os.path.abspath(__file__))+ "/../../lib/")
+from lib.model_registry import ModelRegistry
+
+# Config
+app = FastAPI(title="PHANTOM Pricing Provider")
+app.add_middleware(CORSMiddleware, allow_origins=["*"], allow_credentials=True, allow_methods=["*"], allow_headers=["*"])
+
+supabase: Client = create_client(os.getenv("NEXT_PUBLIC_SUPABASE_URL"), os.getenv("NEXT_PUBLIC_SUPABASE_ANON_KEY"))
+registry = ModelRegistry()
+
+class PriceResponse(BaseModel):
+    productId: str
+    price: float
+    base_price: float
+    markup: float
+    elasticity: Optional[float] = None
+    model_version: str = 'latest'
+
+@app.get("/health")
+def health() -> dict:
+    return {"status": "healthy", "redis": registry.health_check()}
+
+@app.get("/api/{mode}/price/{productId}", response_model=PriceResponse)
+def get_price(mode: Literal['hotel', 'airline'], productId: str, sessionId: Optional[str] = Query(None), experimentId: Optional[str] = Query(None)):
+    product = supabase.table(f'{mode}_products').select("metadata").eq('id', productId).execute().data[0]
+    if not product: raise HTTPException(404, f"Product {productId} not found")
+
+    metadata = product['metadata']
+    base_price = metadata.get('base_price', 100.0)
+
+    class Provider(SupabaseProvider, BackendAPIProvider):
+        def __init__(self, backend_url: str):
+            SupabaseProvider.__init__(self)
+            BackendAPIProvider.__init__(self, backend_url=backend_url)
+
+    context = PipelineContext(
+        provider=Provider(backend_url=os.getenv("BACKEND_URL")),
+        store_mode=mode
+    )
+
+    pricing_model = registry.get_pricing_model('latest')
+    elasticity_df = registry.get_elasticity('latest')
+
+    if pricing_model is None or elasticity_df is None:
+        return PriceResponse(
+            productId=productId,
+            price=base_price,
+            base_price=base_price,
+            markup=1.0,
+            elasticity=None
+        )
+
+    products = context.products
+    if products.empty:
+        raise HTTPException(500, "No products available in catalog")
+
+    # merge elasticity with product base prices
+    products_with_meta = products.copy()
+    products_with_meta['base_price'] = products_with_meta['metadata'].apply(
+        lambda m: m.get('base_price', 100.0) if isinstance(m, dict) else 100.0
+    )
+
+    merged = products_with_meta[['id', 'base_price']].rename(
+        columns={'id': 'productId'}
+    ).merge(
+        elasticity_df[['productId', 'elasticity']],
+        on='productId',
+        how='left'
+    ).fillna({'elasticity': 0.0})
+
+    # compute demand: use pricer's mean_demand if available, else default
+    demand_values = (pricing_model.mean_demand
+                    if hasattr(pricing_model, 'mean_demand') and pricing_model.mean_demand is not None
+                    else np.ones(len(merged)) * 10.0)
+
+    # build state space with session features if sessionId provided
+    session_features = pd.DataFrame()
+    if sessionId:
+        try:
+            # fetch recent session interactions from backend
+            from procesing.steps.session import ExtractSessionFeaturesStep
+            import requests
+            from datetime import datetime, timedelta
+
+            t_end = datetime.utcnow()
+            t_start = t_end - timedelta(hours=1)
+            backend_url = os.getenv("BACKEND_URL")
+            print(backend_url)
+
+            resp = requests.get(
+                f"{os.getenv('BACKEND_URL')}/api/kafka/dump", # TODO: THIS IS SHIT, must fix this
+                params={'topic': 'user-interactions', 't_start': t_start.isoformat(), 't_end': t_end.isoformat()},
+                timeout=2
+            )
+
+            if resp.ok:
+                msgs = resp.json().get('messages', [])
+                interactions_df = pd.DataFrame(msgs)
+
+                if not interactions_df.empty and 'sessionId' in interactions_df.columns:
+                    session_interactions = interactions_df[interactions_df['sessionId'] == sessionId]
+
+                    if not session_interactions.empty:
+                        extractor = ExtractSessionFeaturesStep(context=context)
+                        session_features_df = extractor.transform(session_interactions)
+
+                        if not session_features_df.empty:
+                            session_features = session_features_df.drop(columns=['sessionId'])
+        except Exception as e:
+            print(f"[session-features-error] {e}")
+            # continue without session features
+
+    state = StateSpace(
+        demand=demand_values,
+        prices=merged['base_price'].values,
+        session_features=session_features,
+        product_ids=merged['productId'].values,
+        elasticity=merged['elasticity'].values,
+        metadata={'sessionId': sessionId, 'experimentId': experimentId}
+    )
+
+    oracle = PredictPricesStep(context=context)
+    prices_df = oracle.transform((pricing_model, state))
+
+    product_price_row = prices_df[prices_df['productId'] == productId]
+    if product_price_row.empty:
+        raise HTTPException(404, f"No pricing available for product {productId}")
+
+    optimal_price = float(product_price_row['predicted_price'].iloc[0])
+
+    product_elasticity_row = elasticity_df[elasticity_df['productId'] == productId]
+    product_elasticity = (float(product_elasticity_row['elasticity'].iloc[0])
+                         if not product_elasticity_row.empty else None)
+
+    return PriceResponse(
+        productId=productId,
+        price=optimal_price,
+        base_price=base_price,
+        markup=optimal_price/base_price,
+        elasticity=product_elasticity
+    )
+
+@app.get("/models")
+def list_models(): return registry.list_models()
+
+@app.post("/models/reload")
+def reload_models():
+    elasticity, pricing_model = registry.get_elasticity('latest'), registry.get_pricing_model('latest')
+    return {
+        "elasticity_loaded": bool(elasticity),
+        "n_products": len(elasticity) if elasticity is not None else 0,
+        "pricing_model_loaded": bool(pricing_model),
+        "model_class": pricing_model.__class__.__name__ if pricing_model else None
+    }
+
+if __name__ == "__main__":
+    uvicorn.run(app, host="0.0.0.0", port=int(os.getenv("PROVIDER_PORT", "5001")))

backend/provider/requirements.txt

@@ -0,0 +1,15 @@
+fastapi
+uvicorn[standard]
+pydantic
+numpy
+pandas
+scikit-learn
+redis
+supabase
+confluent-kafka>=2.3.0
+kafka-python
+graphviz
+python-dotenv>=1.0.0
+requests>=2.31.0
+typing-extensions>=4.8.0
+pickle5>=0.0.11; python_version < '3.8'

backend/server/app.py

@@ -7,10 +7,11 @@ import uvicorn
 import os
 import json
 from datetime import datetime
-from kafka import KafkaProducer, KafkaAdminClient
+from kafka import KafkaProducer, KafkaAdminClient, KafkaConsumer
 from kafka.admin import NewTopic
 from kafka.errors import TopicAlreadyExistsError
 from dotenv import load_dotenv
+from supabase import create_client, Client
 load_dotenv()
 
 app = FastAPI()
@@ -18,11 +19,24 @@ app = FastAPI()
 # kafka producer - lazy init
 _producer: Optional[KafkaProducer] = None
 
+# supabase client - lazy init
+_supabase: Optional[Client] = None
+
+def get_supabase() -> Client:
+    global _supabase
+    if _supabase is None:
+        url = os.getenv('NEXT_PUBLIC_SUPABASE_URL')
+        key = os.getenv('NEXT_PUBLIC_SUPABASE_ANON_KEY')
+        if not url or not key:
+            raise ValueError("Supabase credentials not configured")
+        _supabase = create_client(url, key)
+    return _supabase
+
 def get_producer() -> KafkaProducer:
     global _producer
     if _producer is None:
         host = os.getenv('KAFKA_HOST', 'localhost')
-        port = os.getenv('KAFKA_PORT', '29092')  # use internal broker port
+        port = os.getenv('KAFKA_PORT', '9092')
         broker = f'{host}:{port}' if port else host
         print(f"[KAFKA_INIT] Connecting to broker: {broker}")
         _producer = KafkaProducer(
@@ -41,6 +55,7 @@ def get_producer() -> KafkaProducer:
 
 class EventPayload(BaseModel):
     sessionId: str
+    experimentId: Optional[str] = None
     eventName: str
     page: str
     productId: Optional[str] = None
@@ -49,6 +64,14 @@ class EventPayload(BaseModel):
     userAgent: Optional[str] = None
     ts: Optional[str] = None
 
+class PriceLogPayload(BaseModel):
+    productId: str
+    price: float
+    sessionId: str
+    experimentId: Optional[str] = None
+    storeMode: str
+    ts: Optional[str] = None
+
 app.add_middleware(
     CORSMiddleware,
     allow_origins=["*"],
@@ -61,7 +84,7 @@ app.add_middleware(
 async def startup_event():
     """create kafka topics on startup"""
     host = os.getenv('KAFKA_HOST', 'localhost')
-    port = os.getenv('KAFKA_PORT', '29092')
+    port = os.getenv('KAFKA_PORT', '9092')
     broker = f'{host}:{port}'
 
     try:
@@ -72,7 +95,8 @@ async def startup_event():
         )
 
         topics = [
-            NewTopic(name='user-interactions', num_partitions=3, replication_factor=1)
+            NewTopic(name='user-interactions', num_partitions=3, replication_factor=1),
+            NewTopic(name='price-logs', num_partitions=3, replication_factor=1)
         ]
 
         admin.create_topics(new_topics=topics, validate_only=False)
@@ -124,11 +148,212 @@ async def ingest_logs(event: EventPayload):
         print(traceback.format_exc())
         raise HTTPException(status_code=500, detail=str(e))
 
+@app.post("/api/kafka/price-log")
+async def ingest_price_log(price_log: PriceLogPayload):
+    try:
+        if not price_log.ts:
+            price_log.ts = datetime.utcnow().isoformat() + 'Z'
+
+        producer = get_producer()
+        future = producer.send(
+            'price-logs',
+            key=price_log.productId,
+            value=price_log.model_dump()
+        )
+        future.add_errback(lambda e: print(f"[KAFKA_PRICE_LOG_ERROR] {e}"))
+
+        return {"success": True}
+    except Exception as e:
+        import traceback
+        print(f"[PRICE_LOG_ERROR] {e}")
+        print(traceback.format_exc())
+        raise HTTPException(status_code=500, detail=str(e))
+
 @app.get("/api/kafka/dump")
-def dump_logs():
-    # TODO: implement a dump of logs of time period t_start to t_end (params of get)
-    # OR: allow for params of last_n logs as a param - creating two modes of the dumping
-    pass
+def dump_logs(
+    topic: str = 'user-interactions',
+    last_n: Optional[int] = None,
+    t_start: Optional[str] = None,
+    t_end: Optional[str] = None
+):
+    """dump all messages from specified kafka topic
+
+    params:
+        topic: kafka topic to dump (default: user-interactions)
+        last_n: return only last n messages (default: all)
+        t_start: filter by start timestamp iso format
+        t_end: filter by end timestamp iso format
+    """
+    if topic not in ['user-interactions', 'price-logs']:
+        raise HTTPException(status_code=400, detail="Invalid topic")
+
+    host = os.getenv('KAFKA_HOST', 'localhost')
+    port = os.getenv('KAFKA_PORT', '9092')
+    broker = f'{host}:{port}'
+
+    try:
+        consumer = KafkaConsumer(
+            topic,
+            bootstrap_servers=[broker],
+            auto_offset_reset='earliest',
+            enable_auto_commit=False,
+            value_deserializer=lambda x: json.loads(x.decode('utf-8')),
+            consumer_timeout_ms=5000
+        )
+
+        events = []
+        for msg in consumer:
+            events.append(msg.value)
+
+        consumer.close()
+
+        # apply filters
+        if t_start or t_end:
+            filtered = []
+            for e in events:
+                ts = e.get('ts')
+                if ts:
+                    if t_start and ts < t_start:
+                        continue
+                    if t_end and ts > t_end:
+                        continue
+                filtered.append(e)
+            events = filtered
+
+        if last_n and last_n > 0:
+            events = events[-last_n:]
+
+        return {"success": True, "count": len(events), "data": events}
+
+    except Exception as e:
+        import traceback
+        print(f"[DUMP_ERROR] {e}")
+        print(traceback.format_exc())
+        raise HTTPException(status_code=500, detail=str(e))
+
+@app.get("/api/products/{product_id}")
+async def get_product_by_id(product_id: str):
+    """fetch single product by id from either hotel_products or airline_products"""
+    try:
+        supabase = get_supabase()
+
+        # try hotel_products first
+        response = supabase.table('hotel_products').select('*').eq('id', product_id).execute()
+        if response.data and len(response.data) > 0:
+            return {"success": True, "data": response.data[0]}
+
+        # try airline_products
+        response = supabase.table('airline_products').select('*').eq('id', product_id).execute()
+        if response.data and len(response.data) > 0:
+            return {"success": True, "data": response.data[0]}
+
+        raise HTTPException(status_code=404, detail="Product not found")
+
+    except HTTPException:
+        raise
+    except Exception as e:
+        import traceback
+        print(f"[PRODUCT_BY_ID_ERROR] {e}")
+        print(traceback.format_exc())
+        raise HTTPException(status_code=500, detail=str(e))
+
+@app.get("/api/products/type/{product_type}")
+async def get_products(
+    product_type: str,
+    dateIndex: Optional[int] = None,
+    origin: Optional[str] = None,
+    destination: Optional[str] = None,
+    tripType: Optional[str] = None,
+    adults: Optional[int] = None,
+    children: Optional[int] = None,
+    infants: Optional[int] = None,
+    rooms: Optional[int] = None
+):
+    """fetch products from supabase based on type (hotel or airline)
+
+    params:
+        product_type: either 'hotel' or 'airline'
+        dateIndex: optional days offset from today (e.g., 0=today, 1=tomorrow, -1=yesterday)
+        origin: (airline) departure airport code
+        destination: (airline/hotel) arrival airport or hotel location
+        tripType: (airline) roundtrip, oneway, multicity
+        adults, children, infants: passenger counts
+        rooms: (hotel) number of rooms
+    """
+    if product_type not in ['hotel', 'airline']:
+        raise HTTPException(status_code=400, detail="product_type must be 'hotel' or 'airline'")
+
+    try:
+        supabase = get_supabase()
+        table = f'{product_type}_products'
+
+        query = supabase.table(table).select('*')
+
+        # filter by exact date_index if provided
+        if dateIndex is not None:
+            query = query.eq('date_index', dateIndex)
+
+        response = query.execute()
+        results = response.data
+
+        # apply in-memory filters based on metadata for airline products
+        if product_type == 'airline' and results:
+            filtered = []
+            for product in results:
+                metadata = product.get('metadata', {})
+
+                # filter by origin airport
+                if origin:
+                    dep = metadata.get('departure', {})
+                    if dep.get('airport') != origin:
+                        continue
+
+                # filter by destination airport
+                if destination:
+                    arr = metadata.get('arrival', {})
+                    if arr.get('airport') != destination:
+                        continue
+
+                # passenger count validation (ensure total capacity)
+                if adults is not None or children is not None or infants is not None:
+                    total_pax = (adults or 0) + (children or 0) + (infants or 0)
+                    avail = product.get('availability', 0)
+                    if avail < total_pax:
+                        continue
+
+                filtered.append(product)
+
+            results = filtered
+
+        # apply in-memory filters for hotel products
+        elif product_type == 'hotel' and results:
+            filtered = []
+            for product in results:
+                metadata = product.get('metadata', {})
+
+                # filter by occupancy capacity
+                if adults is not None:
+                    max_occ = metadata.get('max_occupancy', 2)
+                    if max_occ < adults:
+                        continue
+
+                # filter by room availability
+                if rooms is not None:
+                    avail = product.get('availability', 0)
+                    if avail < rooms:
+                        continue
+
+                filtered.append(product)
+
+            results = filtered
+
+        return {"success": True, "count": len(results), "data": results}
+
+    except Exception as e:
+        import traceback
+        print(f"[PRODUCTS_ERROR] {e}")
+        print(traceback.format_exc())
+        raise HTTPException(status_code=500, detail=str(e))
 
 
 

backend/server/requirements.txt

@@ -3,3 +3,4 @@ uvicorn[standard]==0.24.0
 kafka-python==2.0.2
 pydantic==2.5.0
 python-dotenv==1.0.0
+supabase==2.9.1

docker-compose.yml

@@ -9,6 +9,9 @@ services:
     environment:
       - KAFKA_HOST=kafka
       - KAFKA_PORT=29092
+      - BACKEND_PORT=5000
+      - NEXT_PUBLIC_SUPABASE_URL=${NEXT_PUBLIC_SUPABASE_URL}
+      - NEXT_PUBLIC_SUPABASE_ANON_KEY=${NEXT_PUBLIC_SUPABASE_ANON_KEY}
     depends_on:
       - kafka
     restart: unless-stopped
@@ -68,6 +71,153 @@ services:
       - "${REDPANDA_CONSOLE_PORT:-8080}:8080"
     restart: unless-stopped
 
+  postgres:
+    container_name: "PHANTOM-postgres"
+    image: postgres:13
+    environment:
+      - POSTGRES_USER=airflow
+      - POSTGRES_PASSWORD=airflow
+      - POSTGRES_DB=airflow
+    ports:
+      - "5433:5432"
+    volumes:
+      - postgres_data:/var/lib/postgresql/data
+    restart: unless-stopped
+
+  airflow-init:
+    container_name: "PHANTOM-airflow-init"
+    build:
+      context: .
+      dockerfile: docker/Airflow.dockerfile
+    depends_on:
+      - postgres
+    environment:
+      - AIRFLOW__CORE__EXECUTOR=SequentialExecutor
+      - AIRFLOW__DATABASE__SQL_ALCHEMY_CONN=postgresql+psycopg2://airflow:airflow@postgres/airflow
+      - AIRFLOW__CORE__FERNET_KEY=${AIRFLOW_FERNET_KEY}
+      - AIRFLOW__CORE__LOAD_EXAMPLES=false
+      - AIRFLOW__CORE__ENABLE_XCOM_PICKLING=true
+      - _AIRFLOW_DB_MIGRATE=true
+      - _AIRFLOW_WWW_USER_CREATE=true
+      - _AIRFLOW_WWW_USER_USERNAME=admin
+      - _AIRFLOW_WWW_USER_PASSWORD=admin
+      - REDIS_HOST=redis
+      - REDIS_PORT=6379
+    volumes:
+      - ./experiments/airflow/dags:/opt/airflow/dags
+      - ./experiments/airflow/logs:/opt/airflow/logs
+      - ./experiments/airflow/plugins:/opt/airflow/plugins
+      - ./experiments/procesing:/opt/airflow/procesing
+      - ./lib:/opt/airflow/lib
+    command: version
+    restart: "no"
+
+  airflow-webserver:
+    container_name: "PHANTOM-airflow-webserver"
+    build:
+      context: .
+      dockerfile: docker/Airflow.dockerfile
+    depends_on:
+      - postgres
+      - airflow-init
+      - redis
+    environment:
+      - AIRFLOW__CORE__EXECUTOR=SequentialExecutor
+      - AIRFLOW__DATABASE__SQL_ALCHEMY_CONN=postgresql+psycopg2://airflow:airflow@postgres/airflow
+      - AIRFLOW__CORE__FERNET_KEY=${AIRFLOW_FERNET_KEY}
+      - AIRFLOW__CORE__DAGS_ARE_PAUSED_AT_CREATION=true
+      - AIRFLOW__CORE__LOAD_EXAMPLES=false
+      - AIRFLOW__CORE__ENABLE_XCOM_PICKLING=true
+      - AIRFLOW__WEBSERVER__EXPOSE_CONFIG=true
+      - KAFKA_HOST=kafka
+      - KAFKA_PORT=29092
+      - BACKEND_URL=http://backend:5000
+      - NEXT_PUBLIC_SUPABASE_URL=${NEXT_PUBLIC_SUPABASE_URL}
+      - NEXT_PUBLIC_SUPABASE_ANON_KEY=${NEXT_PUBLIC_SUPABASE_ANON_KEY}
+      - REDIS_HOST=redis
+      - REDIS_PORT=6379
+    ports:
+      - "${AIRFLOW_WEBSERVER_PORT:-8085}:8080"
+    volumes:
+      - ./experiments/airflow/dags:/opt/airflow/dags:ro
+      - ./experiments/airflow/logs:/opt/airflow/logs
+      - ./experiments/airflow/plugins:/opt/airflow/plugins:ro
+      - ./experiments/procesing:/opt/airflow/procesing:ro
+      - ./lib:/opt/airflow/lib:ro
+    command: webserver
+    restart: unless-stopped
+    healthcheck:
+      test: ["CMD", "curl", "--fail", "http://localhost:8080/health"]
+      interval: 30s
+      timeout: 10s
+      retries: 5
+      start_period: 30s
+
+  airflow-scheduler:
+    container_name: "PHANTOM-airflow-scheduler"
+    build:
+      context: .
+      dockerfile: docker/Airflow.dockerfile
+    depends_on:
+      airflow-webserver:
+        condition: service_healthy
+      redis:
+        condition: service_started
+    environment:
+      - AIRFLOW__CORE__EXECUTOR=SequentialExecutor
+      - AIRFLOW__DATABASE__SQL_ALCHEMY_CONN=postgresql+psycopg2://airflow:airflow@postgres/airflow
+      - AIRFLOW__CORE__FERNET_KEY=${AIRFLOW_FERNET_KEY}
+      - AIRFLOW__CORE__DAGS_ARE_PAUSED_AT_CREATION=true
+      - AIRFLOW__CORE__LOAD_EXAMPLES=false
+      - AIRFLOW__CORE__ENABLE_XCOM_PICKLING=true
+      - KAFKA_HOST=kafka
+      - KAFKA_PORT=29092
+      - BACKEND_URL=http://backend:5000
+      - NEXT_PUBLIC_SUPABASE_URL=${NEXT_PUBLIC_SUPABASE_URL}
+      - NEXT_PUBLIC_SUPABASE_ANON_KEY=${NEXT_PUBLIC_SUPABASE_ANON_KEY}
+      - REDIS_HOST=redis
+      - REDIS_PORT=6379
+    volumes:
+      - ./experiments/airflow/dags:/opt/airflow/dags:ro
+      - ./experiments/airflow/logs:/opt/airflow/logs
+      - ./experiments/airflow/plugins:/opt/airflow/plugins:ro
+      - ./experiments/procesing:/opt/airflow/procesing:ro
+      - ./lib:/opt/airflow/lib:ro
+    command: scheduler
+    restart: unless-stopped
+    healthcheck:
+      test: ["CMD-SHELL", 'airflow jobs check --job-type SchedulerJob --hostname "$${HOSTNAME}"']
+      interval: 30s
+      timeout: 10s
+      retries: 5
+      start_period: 30s
+
+  pricing-provider:
+    container_name: "PHANTOM-pricing-provider"
+    build:
+      context: .
+      dockerfile: docker/Provider.dockerfile
+    depends_on:
+      - redis
+      - kafka
+    environment:
+      - PROVIDER_PORT=5001
+      - REDIS_HOST=redis
+      - REDIS_PORT=6379
+      - KAFKA_HOST=kafka
+      - KAFKA_PORT=29092
+      - NEXT_PUBLIC_SUPABASE_URL=${NEXT_PUBLIC_SUPABASE_URL}
+      - NEXT_PUBLIC_SUPABASE_ANON_KEY=${NEXT_PUBLIC_SUPABASE_ANON_KEY}
+    ports:
+      - "${PROVIDER_PORT:-5001}:5001"
+    volumes:
+      - ./lib:/app/lib:ro
+      - ./experiments/procesing:/app/procesing:ro
+      - ./backend/provider:/app/provider:ro
+    command: python -m uvicorn provider.app:app --host 0.0.0.0 --port 5001
+    restart: unless-stopped
+
 volumes:
   phantom_kafka_data:
   phantom_redis_data:
+  postgres_data:

docker/Airflow.dockerfile

@@ -0,0 +1,23 @@
+FROM apache/airflow:2.7.3-python3.11
+
+USER root
+
+# install system deps if needed
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    build-essential \
+    && apt-get clean \
+    && rm -rf /var/lib/apt/lists/*
+
+USER airflow
+
+# copy requirements for pipeline dependencies
+COPY requirements.txt /tmp/requirements.txt
+RUN pip install --no-cache-dir -r /tmp/requirements.txt
+
+# install postgres driver and providers
+RUN pip install --no-cache-dir \
+    psycopg2-binary \
+    apache-airflow-providers-postgres
+
+# set airflow home
+ENV AIRFLOW_HOME=/opt/airflow

docker/Provider.dockerfile

@@ -0,0 +1,24 @@
+FROM python:3.11-slim
+
+WORKDIR /app
+
+# Install system dependencies including graphviz
+RUN apt-get update && apt-get install -y \
+    gcc \
+    g++ \
+    graphviz \
+    libgraphviz-dev \
+    && rm -rf /var/lib/apt/lists/*
+
+# Copy and install Python dependencies
+COPY backend/provider/requirements.txt /app/
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Structure will be mounted via volumes:
+# /app/lib -> lib/
+# /app/procesing -> experiments/procesing/
+# /app/provider -> backend/provider/
+
+ENV PYTHONPATH=/app:/app/lib:/app/procesing
+
+CMD ["python", "-m", "uvicorn", "provider.app:app", "--host", "0.0.0.0", "--port", "5001"]

experiments/README.md

@@ -0,0 +1,8 @@
+
+# Products
+# Agents
+# Pipeline
+
+Our pipeline technically should follow principles in a style like this:
+- Each step should be defined as an inheriting child of an scikit pipeline step, the granularity of the steps is dictated by the following: a step should be a transformation, augmentation or computation independently, no single stage should run multiple in-itself. This way we can modularize properly all the components and track properly in airflow. A stage can be defined as an sklearn step but then must be transalted to a function that takes the context in our DAG of airflow. All parametrization must be done via contexts.
+

experiments/agents/__init__.py

@@ -0,0 +1 @@
+"""Agentic behavior runner for PHANTOM research platform."""

experiments/agents/agent.py

@@ -0,0 +1,47 @@
+from .base import Agent as BaseAgent
+from browser_use import Browser, Agent, ChatOpenAI
+from enum import Enum
+
+class AgentTypes(str, Enum):
+    GENERIC_BROWSER_USE_AGENT = "generic_browser_use_agent"
+
+def _build_prompt(goal : str, environment_url : str) -> str:
+    #TODO: Improve prompt engineering here and experiment with
+    return f"""You are an autonomous agent tasked with achieving the following goal: {goal}
+You have access to a web browser to interact with the environment at {environment_url}.
+Use the browser to navigate, gather information, and perform actions necessary to accomplish your goal.
+Be thorough and ensure you complete the task fully."""
+
+class GenericBrowserUseAgent(BaseAgent):
+    def __init__(self,
+                 goal: str,
+                 url: str = "http://localhost:3000",
+                 timeout: int = 300,
+                 llm_model: str = "gpt-5-mini",
+                 headless: bool = True):
+        super().__init__(goal, url, timeout)
+        self.llm_model = ChatOpenAI(model=llm_model)
+        self.browser = Browser(headless=headless)
+        self.agent = Agent(task=_build_prompt(goal, url),
+                           llm=self.llm_model,
+                           browser=self.browser)
+    async def act(self) -> str:
+        self.result = await self.agent.run()
+        # https://github.com/browser-use/browser-use/blob/main/browser_use/agent/views.py#L301
+        return self.result.final_result()
+
+def get_agent(agent_type: AgentTypes,  **kwargs) -> Agent:
+    if agent_type == AgentTypes.GENERIC_BROWSER_USE_AGENT:
+        return GenericBrowserUseAgent(**kwargs)
+    else:
+        raise ValueError(f"Unknown agent type: {agent_type}")
+
+if __name__ == "__main__":
+    import asyncio
+    JTBD= "Find me the cheapest room in Madrid for 2 people in the next two days, review each hotel room in detail and then add it to cart."
+    agent = get_agent(AgentTypes.GENERIC_BROWSER_USE_AGENT,
+                      goal=JTBD,
+                      url="http://localhost:3000/start-task?uuid=d10f5ab3-a7b7-4e97-8d94-ab06f1537c0a",
+                      timeout=300)
+    R=asyncio.run(agent.act())
+    print(R)

experiments/agents/base.py

@@ -0,0 +1,19 @@
+from abc import ABC, abstractmethod
+from typing import Optional
+
+class Agent(ABC):
+    """Base interface for browser automation agents"""
+
+    def __init__(self, goal: str, url: str = "http://localhost:3000", timeout: int = 300):
+        self.goal = goal
+        self.url = url
+        self.timeout = timeout
+        self.result: Optional[str] = None
+
+    @abstractmethod
+    async def act(self) -> str:
+        """Execute goal and return result text"""
+        pass
+
+    def final_result(self) -> Optional[str]:
+        return self.result

experiments/agents/test.py

@@ -0,0 +1,30 @@
+import pytest
+import asyncio
+from experiments.agents.agent import get_agent, AgentTypes
+import os
+
+
+def test_agent_init():
+    agent = get_agent(AgentTypes.GENERIC_BROWSER_USE_AGENT, goal="test", url="http://example.com", timeout=100)
+    assert agent.goal == "test"
+    assert agent.url == "http://example.com"
+    assert agent.timeout == 100
+
+
+def test_invalid_agent():
+    with pytest.raises(ValueError):
+        get_agent("invalid", goal="test")
+
+
+@pytest.mark.asyncio
+@pytest.mark.skipif("OPENAI_API_KEY" not in os.environ, reason="OPENAI_API_KEY not set")
+async def test_agent_execution():
+    agent = get_agent(AgentTypes.GENERIC_BROWSER_USE_AGENT, goal="get page title", url="https://example.com", timeout=60)
+
+    result = await agent.act()
+    assert result
+    assert agent.final_result()
+    assert agent.final_result().history[-1].result[-1].is_done == True
+    assert isinstance(result, str)
+    assert "example" in result.lower()
+    assert len(result) > 0

experiments/airflow/dags/elasticity_pricing_dag.py

@@ -0,0 +1,346 @@
+from airflow import DAG
+from airflow.operators.python import PythonOperator
+from airflow.utils.dates import days_ago
+from datetime import timedelta
+import pandas as pd
+import logging
+import sys
+import pickle
+import io
+
+# add parent dir to path so procesing package can be imported
+sys.path.insert(0, '/opt/airflow')
+
+from procesing.context import PipelineContext
+from procesing.providers import SupabaseProvider, BackendAPIProvider
+from procesing.steps import (
+    FetchInteractionsStep,
+    FetchPriceLogsStep,
+    CreatePriceBucketsStep,
+    AugmentEventNamesStep,
+    ChunkByTimeWindowStep,
+    ComputeDemandForChunksStep,
+    AggregatePriceLogsStep,
+    ComputeElasticityStep,
+    BuildStateSpaceStep,
+    FitPricingFunctionStep,
+    PredictPricesStep,
+)
+
+default_args = {
+    'owner': 'phantom-research',
+    'depends_on_past': False,
+    'email_on_failure': False,
+    'email_on_retry': False,
+    'retries': 2,
+    'retry_delay': timedelta(minutes=5),
+}
+
+def get_provider():
+    """Factory to create composite provider"""
+    class CompositeProvider(SupabaseProvider, BackendAPIProvider):
+        def __init__(self):
+            SupabaseProvider.__init__(self)
+            BackendAPIProvider.__init__(self)
+    return CompositeProvider()
+
+def get_context(**kwargs):
+    """Build pipeline context from Airflow config"""
+    dag_conf = kwargs.get('dag_run').conf if kwargs.get('dag_run') else {}
+    return PipelineContext(
+        provider=get_provider(),
+        store_mode=dag_conf.get('store_mode', 'hotel'),
+        window_size=dag_conf.get('window_size', '30s'),
+        n_price_buckets=dag_conf.get('n_price_buckets', 5),
+        elasticity_method=dag_conf.get('elasticity_method', 'point'),
+        min_observations=dag_conf.get('min_observations', 2),
+    )
+
+# atomic task functions (each wraps one sklearn step)
+def fetch_interactions(**kwargs):
+    """Task: Fetch interaction data from Kafka"""
+    context = get_context(**kwargs)
+    step = FetchInteractionsStep(context)
+    df = step.transform(None)
+
+    kwargs['ti'].xcom_push(key='interactions_raw', value=pickle.dumps(df))
+    logging.info(f"Fetched {len(df)} interaction records")
+    return len(df)
+
+def fetch_price_logs(**kwargs):
+    """Task: Fetch price logs from Kafka"""
+    context = get_context(**kwargs)
+    step = FetchPriceLogsStep(context)
+    df = step.transform(None)
+
+    kwargs['ti'].xcom_push(key='price_logs_raw', value=pickle.dumps(df))
+    logging.info(f"Fetched {len(df)} price records")
+    return len(df)
+
+def create_price_buckets(**kwargs):
+    """Task: Create price buckets for interactions"""
+    ti = kwargs['ti']
+    df = pickle.loads(ti.xcom_pull(key='interactions_raw'))
+
+    context = get_context(**kwargs)
+    step = CreatePriceBucketsStep(context)
+    df = step.transform(df)
+
+    ti.xcom_push(key='interactions_bucketed', value=pickle.dumps(df))
+    logging.info(f"Created price buckets for {len(df)} interactions")
+    return len(df)
+
+def augment_event_names(**kwargs):
+    """Task: Augment event names with product and price schema"""
+    ti = kwargs['ti']
+    df = pickle.loads(ti.xcom_pull(key='interactions_bucketed'))
+
+    context = get_context(**kwargs)
+    step = AugmentEventNamesStep(context)
+    df = step.transform(df)
+
+    ti.xcom_push(key='interactions_final', value=pickle.dumps(df))
+    logging.info(f"Augmented event names for {len(df)} interactions")
+    return len(df)
+
+def chunk_interactions(**kwargs):
+    """Task: Chunk interactions into time windows"""
+    ti = kwargs['ti']
+    df = pickle.loads(ti.xcom_pull(key='interactions_final'))
+
+    context = get_context(**kwargs)
+    step = ChunkByTimeWindowStep(context)
+    chunks = step.transform(df)
+
+    ti.xcom_push(key='interaction_chunks', value=pickle.dumps(chunks))
+    logging.info(f"Generated {len(chunks)} interaction chunks")
+    return len(chunks)
+
+def compute_demand(**kwargs):
+    """Task: Compute demand vectors for all chunks"""
+    ti = kwargs['ti']
+    chunks = pickle.loads(ti.xcom_pull(key='interaction_chunks'))
+
+    context = get_context(**kwargs)
+    step = ComputeDemandForChunksStep(context)
+    demand_chunks = step.transform(chunks)
+
+    ti.xcom_push(key='demand_chunks', value=pickle.dumps(demand_chunks))
+    logging.info(f"Computed demand for {len(demand_chunks)} chunks")
+    return len(demand_chunks)
+
+def aggregate_price_logs(**kwargs):
+    """Task: Aggregate price logs into time windows """
+    ti = kwargs['ti']
+    df = pickle.loads(ti.xcom_pull(key='price_logs_raw'))
+
+    context = get_context(**kwargs)
+    step = AggregatePriceLogsStep(context)
+    price_chunks = step.transform(df)
+
+    ti.xcom_push(key='price_chunks', value=pickle.dumps(price_chunks))
+    logging.info(f"Aggregated {len(price_chunks)} price chunks")
+    return len(price_chunks)
+
+def compute_elasticity(**kwargs):
+    """Task: Compute price elasticity from demand and price chunks"""
+    ti = kwargs['ti']
+    demand_chunks = pickle.loads(ti.xcom_pull(key='demand_chunks'))
+    price_chunks = pickle.loads(ti.xcom_pull(key='price_chunks'))
+
+    context = get_context(**kwargs)
+    step = ComputeElasticityStep(context)
+    elasticity_df = step.transform((demand_chunks, price_chunks))
+
+    ti.xcom_push(key='elasticity_results', value=pickle.dumps(elasticity_df))
+    logging.info(f"Computed elasticity for {len(elasticity_df)} products")
+
+    return {
+        'n_products': len(elasticity_df),
+        'mean_elasticity': float(elasticity_df['elasticity'].mean()),
+        'median_elasticity': float(elasticity_df['elasticity'].median())
+    }
+
+def build_state_space(**kwargs):
+    """Task: Build state space from elasticity"""
+    ti = kwargs['ti']
+    elasticity_df = pickle.loads(ti.xcom_pull(key='elasticity_results'))
+
+    context = get_context(**kwargs)
+    step = BuildStateSpaceStep(context)
+    state_space = step.transform(elasticity_df)
+
+    ti.xcom_push(key='state_space', value=pickle.dumps(state_space))
+    logging.info("Built state space for pricing")
+    return True
+
+def fit_pricing_function(**kwargs):
+    """Task: Fit pricing function using elasticity"""
+    ti = kwargs['ti']
+    elasticity_df = pickle.loads(ti.xcom_pull(key='elasticity_results'))
+
+    context = get_context(**kwargs)
+    step = FitPricingFunctionStep(context)
+    pricer = step.transform(elasticity_df)
+
+    ti.xcom_push(key='pricer', value=pickle.dumps(pricer))
+    logging.info("Fitted pricing function")
+    return True
+
+def predict_prices(**kwargs):
+    """Task: Predict optimal prices"""
+    ti = kwargs['ti']
+    pricer = pickle.loads(ti.xcom_pull(key='pricer'))
+    state_space = pickle.loads(ti.xcom_pull(key='state_space'))
+
+    context = get_context(**kwargs)
+    step = PredictPricesStep(context)
+    prices_df = step.transform((pricer, state_space))
+
+    ti.xcom_push(key='predicted_prices', value=pickle.dumps(prices_df))
+    logging.info(f"Predicted prices for {len(prices_df)} products")
+    return len(prices_df)
+
+def publish_results(**kwargs):
+    """Task: Publish elasticity and pricing results to model registry"""
+    ti = kwargs['ti']
+    elasticity_df = pickle.loads(ti.xcom_pull(key='elasticity_results'))
+    prices_df = pickle.loads(ti.xcom_pull(key='predicted_prices'))
+
+    sys.path.insert(0, '/opt/airflow')
+    from lib.model_registry import ModelRegistry
+
+    registry = ModelRegistry()
+    dag_conf = kwargs.get('dag_run').conf if kwargs.get('dag_run') else {}
+
+    metadata = {
+        'timestamp': pd.Timestamp.now().isoformat(),
+        'window_size': dag_conf.get('window_size', '30s'),
+        'store_mode': dag_conf.get('store_mode', 'hotel'),
+        'dag_run_id': kwargs['dag_run'].run_id if kwargs.get('dag_run') else 'manual'
+    }
+
+    registry.publish_elasticity(elasticity_df, model_name='latest', metadata=metadata)
+
+    # get fitted pricer from XCom
+    pricer = pickle.loads(ti.xcom_pull(key='pricer'))
+    registry.publish_pricing_model(
+        pricer,
+        model_name='latest',
+        metadata={**metadata, 'model_type': type(pricer).__name__}
+    )
+
+    logging.info(f"Published elasticity + pricing for {len(elasticity_df)} products")
+
+    return {
+        'n_products': len(elasticity_df),
+        'registry_status': 'success',
+        'elasticity_mean': float(elasticity_df['elasticity'].mean())
+    }
+
+
+# DAG definition
+with DAG(
+    'elasticity_pricing_pipeline',
+    default_args=default_args,
+    description='E2E refactored pipeline: atomic steps with proper separation',
+    schedule_interval='*/15 * * * *',
+    start_date=days_ago(1),
+    catchup=False,
+    max_active_runs=1,
+    tags=['pricing', 'elasticity', 'research', 'refactored'],
+) as dag:
+
+    # parallel data fetching
+    t_fetch_interactions = PythonOperator(
+        task_id='fetch_interactions',
+        python_callable=fetch_interactions,
+        provide_context=True,
+    )
+
+    t_fetch_price_logs = PythonOperator(
+        task_id='fetch_price_logs',
+        python_callable=fetch_price_logs,
+        provide_context=True,
+    )
+
+    # interaction processing branch
+    t_create_buckets = PythonOperator(
+        task_id='create_price_buckets',
+        python_callable=create_price_buckets,
+        provide_context=True,
+    )
+
+    t_augment_events = PythonOperator(
+        task_id='augment_event_names',
+        python_callable=augment_event_names,
+        provide_context=True,
+    )
+
+    t_chunk_interactions = PythonOperator(
+        task_id='chunk_interactions',
+        python_callable=chunk_interactions,
+        provide_context=True,
+    )
+
+    t_compute_demand = PythonOperator(
+        task_id='compute_demand',
+        python_callable=compute_demand,
+        provide_context=True,
+    )
+
+    # price processing branch (VECTORIZED)
+    t_aggregate_prices = PythonOperator(
+        task_id='aggregate_price_logs',
+        python_callable=aggregate_price_logs,
+        provide_context=True,
+    )
+
+    # convergence: compute elasticity
+    t_compute_elasticity = PythonOperator(
+        task_id='compute_elasticity',
+        python_callable=compute_elasticity,
+        provide_context=True,
+    )
+
+    # pricing tasks
+    t_build_state = PythonOperator(
+        task_id='build_state_space',
+        python_callable=build_state_space,
+        provide_context=True,
+    )
+
+    t_fit_pricer = PythonOperator(
+        task_id='fit_pricing_function',
+        python_callable=fit_pricing_function,
+        provide_context=True,
+    )
+
+    t_predict_prices = PythonOperator(
+        task_id='predict_prices',
+        python_callable=predict_prices,
+        provide_context=True,
+    )
+
+    # publish to registry
+    t_publish = PythonOperator(
+        task_id='publish_results',
+        python_callable=publish_results,
+        provide_context=True,
+    )
+
+    # dependency graph (clear atomic flow)
+    # parallel fetches
+    [t_fetch_interactions, t_fetch_price_logs]
+
+    # interaction branch: fetch -> bucket -> augment -> chunk -> demand
+    t_fetch_interactions >> t_create_buckets >> t_augment_events >> t_chunk_interactions >> t_compute_demand
+
+    # price branch: fetch -> aggregate (vectorized)
+    t_fetch_price_logs >> t_aggregate_prices
+
+    # convergence: both branches -> elasticity
+    [t_compute_demand, t_aggregate_prices] >> t_compute_elasticity
+
+    # pricing: elasticity -> state + fit -> predict -> publish
+    t_compute_elasticity >> [t_build_state, t_fit_pricer] >> t_predict_prices >> t_publish

experiments/procesing/__init__.py

@@ -0,0 +1,55 @@
+from procesing.context import PipelineContext
+from procesing.providers import DataProvider, SupabaseProvider, BackendAPIProvider
+from procesing.steps import (
+    BaseContextStep,
+    FetchInteractionsStep,
+    FetchPriceLogsStep,
+    FetchExperimentsStep,
+    JoinExperimentsStep,
+    CreatePriceBucketsStep,
+    AugmentEventNamesStep,
+    ChunkByTimeWindowStep,
+    ComputeDemandStep,
+    ComputeDemandForChunksStep,
+    AggregatePriceLogsStep,
+    ComputeElasticityStep,
+    StateSpace,
+    BuildStateSpaceStep,
+    FitPricingFunctionStep,
+    PredictPricesStep,
+)
+from procesing.pipelines import (
+    interaction_extraction_pipeline,
+    price_extraction_pipeline,
+    elasticity_computation_pipeline,
+    pricing_pipeline,
+    full_pipeline,
+)
+
+__all__ = [
+    'PipelineContext',
+    'DataProvider',
+    'SupabaseProvider',
+    'BackendAPIProvider',
+    'BaseContextStep',
+    'FetchInteractionsStep',
+    'FetchPriceLogsStep',
+    'FetchExperimentsStep',
+    'JoinExperimentsStep',
+    'CreatePriceBucketsStep',
+    'AugmentEventNamesStep',
+    'ChunkByTimeWindowStep',
+    'ComputeDemandStep',
+    'ComputeDemandForChunksStep',
+    'AggregatePriceLogsStep',
+    'ComputeElasticityStep',
+    'StateSpace',
+    'BuildStateSpaceStep',
+    'FitPricingFunctionStep',
+    'PredictPricesStep',
+    'interaction_extraction_pipeline',
+    'price_extraction_pipeline',
+    'elasticity_computation_pipeline',
+    'pricing_pipeline',
+    'full_pipeline',
+]

experiments/procesing/context.py

@@ -0,0 +1,34 @@
+from typing import Any, Dict
+import pandas as pd
+from procesing.providers.base import DataProvider
+
+class PipelineContext:
+    """
+    Context for pipeline execution holding config, provider, and cached data.
+    Enables dependency injection and eliminates global state.
+    """
+
+    def __init__(self,
+                 provider: DataProvider,
+                 store_mode: str,
+                 window_size: str = '30s',
+                 **config):
+        self.provider = provider
+        self.store_mode = store_mode
+        self.window_size = window_size
+        self.config = config
+        self._cache: Dict[str, Any] = {}
+
+    def get_cached(self, key: str, default=None):
+        return self._cache.get(key, default)
+
+    def cache(self, key: str, value):
+        self._cache[key] = value
+        return value
+
+    @property
+    def products(self) -> pd.DataFrame:
+        """Lazy-load and cache product catalog, single fetch per pipeline run"""
+        if 'products' not in self._cache:
+            self._cache['products'] = self.provider.fetch_products(self.store_mode)
+        return self._cache['products']

experiments/procesing/elasticity.py

@@ -0,0 +1,332 @@
+import numpy as np
+import pandas as pd
+from typing import List, Dict, Optional
+from sklearn.base import BaseEstimator, TransformerMixin
+from supabase import create_client, Client
+import os
+
+SUPABASE_URL = os.getenv("NEXT_PUBLIC_SUPABASE_URL", "")
+SUPABASE_KEY = os.getenv("NEXT_PUBLIC_SUPABASE_ANON_KEY", "")
+
+supabase: Client = create_client(SUPABASE_URL, SUPABASE_KEY)
+
+class TemporalElasticityEstimator(BaseEstimator, TransformerMixin):
+    """
+    Compute price elasticity from time-series demand and price data.
+
+    Elasticity = (% change in quantity) / (% change in price)
+
+    Works with chunked time-window data from ChunkInteractionsIntoSteps.
+    """
+
+    def __init__(self,
+                 method:str='point',
+                 min_observations:int=2,
+                 smooth_window:Optional[int]=None):
+        """
+        Args:
+            method: 'point' (point elasticity) or 'arc' (arc elasticity)
+            min_observations: min data points needed per product
+            smooth_window: if set, apply rolling avg smoothing to time series
+        """
+        self.method = method
+        self.min_observations = min_observations
+        self.smooth_window = smooth_window
+
+    def fit(self, X):
+        return self
+
+    def transform(self,
+                  demand_chunks: List[Dict],
+                  price_chunks: List[Dict],
+                  store_mode: str = 'hotel') -> pd.DataFrame:
+        """
+        Args:
+            demand_chunks: list from ChunkInteractionsIntoSteps + DemandEstimator
+                           each item: {'window_start', 'window_end', 'demand_vector'}
+            price_chunks: list of dicts with {'window_start', 'window_end', 'price_vector'}
+            store_mode: 'hotel' or 'airline' to fetch all products
+
+        Returns:
+            df with [productId, elasticity, std_error, n_observations]
+        """
+        # fetch all products from database
+        all_products = supabase.table(f'{store_mode}_products').select("id").execute()
+        all_product_ids = [p['id'] for p in all_products.data]
+
+        aligned = self._align_chunks(demand_chunks, price_chunks)
+        if not aligned:
+            # return all products with zero elasticity
+            return pd.DataFrame({
+                'productId': all_product_ids,
+                'elasticity': 0.0,
+                'std_error': 0.0,
+                'n_obs': 0
+            })
+
+        # build time series per product
+        product_series = self._build_product_timeseries(aligned)
+
+        # compute elasticity per product
+        elasticities = []
+        for pid, series in product_series.items():
+            if len(series) < self.min_observations:
+                # assign 0 elasticity for products with insufficient data
+                elasticities.append({
+                    'productId': pid,
+                    'elasticity': 0.0,
+                    'std_error': 0.0,
+                    'n_obs': len(series)
+                })
+                continue
+
+            # apply smoothing if requested
+            if self.smooth_window and len(series) >= self.smooth_window:
+                series = self._smooth_series(series, self.smooth_window)
+
+            elast = self._compute_elasticity(series)
+            elasticities.append({
+                'productId': pid,
+                'elasticity': elast['value'],
+                'std_error': elast.get('std_error', 0.0),
+                'n_obs': len(series)
+            })
+
+        result_df = pd.DataFrame(elasticities)
+
+        # fill in missing products with zero elasticity
+        observed_pids = set(result_df['productId'].unique())
+        missing_pids = [pid for pid in all_product_ids if pid not in observed_pids]
+
+        if missing_pids:
+            missing_df = pd.DataFrame({
+                'productId': missing_pids,
+                'elasticity': 0.0,
+                'std_error': 0.0,
+                'n_obs': 0
+            })
+            result_df = pd.concat([result_df, missing_df], ignore_index=True)
+
+        return result_df
+
+    def _align_chunks(self, demand_chunks, price_chunks):
+        """Align demand and price data by matching time windows."""
+        aligned = []
+
+        # create lookup for price chunks by window_start
+        price_lookup = {chunk['window_start']: chunk for chunk in price_chunks}
+
+        for demand_chunk in demand_chunks:
+            window_start = demand_chunk['window_start']
+            if window_start in price_lookup:
+                aligned.append({
+                    'window_start': window_start,
+                    'window_end': demand_chunk['window_end'],
+                    'demand': demand_chunk['demand_vector'],
+                    'prices': price_lookup[window_start]['price_vector']
+                })
+
+        return aligned
+
+    def _build_product_timeseries(self, aligned_chunks):
+        """Build time series [price, quantity] per product."""
+        # vectorize chunk merging instead of iterating rows
+        all_merged = []
+        for chunk in aligned_chunks:
+            merged = chunk['demand'].merge(chunk['prices'], on='productId', how='inner')
+            merged['timestamp'] = chunk['window_start']
+            all_merged.append(merged[['productId', 'timestamp', 'price', 'demand_score']])
+
+        if not all_merged:
+            return {}
+
+        # concat all chunks and group by productId in one pass
+        combined = pd.concat(all_merged, ignore_index=True)
+        series_by_product = {
+            pid: group[['timestamp', 'price', 'demand_score']].rename(
+                columns={'demand_score': 'quantity'}
+            ).to_dict('records')
+            for pid, group in combined.groupby('productId')
+        }
+
+        return series_by_product
+
+    def _smooth_series(self, series, window):
+        """Apply rolling average smoothing."""
+        df = pd.DataFrame(series)
+        df['price_smooth'] = df['price'].rolling(window=window, center=True).mean()
+        df['quantity_smooth'] = df['quantity'].rolling(window=window, center=True).mean()
+        df = df.dropna()
+
+        return [{'timestamp': row['timestamp'],
+                 'price': row['price_smooth'],
+                 'quantity': row['quantity_smooth']}
+                for _, row in df.iterrows()]
+
+    def _compute_elasticity(self, series):
+        """Compute elasticity from time series."""
+        if len(series) < 2:
+            return {'value': 0.0, 'std_error': 0.0}
+
+        prices = np.array([s['price'] for s in series])
+        quantities = np.array([s['quantity'] for s in series])
+
+        # filter out zero/negative values
+        valid = (prices > 0) & (quantities > 0)
+        if valid.sum() < 2:
+            return {'value': 0.0, 'std_error': 0.0}
+
+        prices = prices[valid]
+        quantities = quantities[valid]
+
+        if self.method == 'point':
+            return self._point_elasticity(prices, quantities)
+        elif self.method == 'arc':
+            return self._arc_elasticity(prices, quantities)
+        else:
+            raise ValueError(f"Unknown method: {self.method}")
+
+    def _point_elasticity(self, prices, quantities):
+        """
+        Point elasticity using log-log regression.
+        log(Q) = a + b*log(P), elasticity = b
+        """
+        if len(prices) < 2:
+            return {'value': 0.0, 'std_error': 0.0}
+
+        log_p = np.log(prices)
+        log_q = np.log(quantities)
+
+        # simple linear regression
+        if log_p.std() == 0:
+            return {'value': 0.0, 'std_error': 0.0}
+
+        cov = np.cov(log_p, log_q)[0, 1]
+        var = np.var(log_p)
+        b = cov / var
+
+        # std error estimate (avoid div by zero)
+        if len(prices) <= 2:
+            se_b = 0.0
+        else:
+            residuals = log_q - (log_q.mean() + b * (log_p - log_p.mean()))
+            mse = (residuals ** 2).sum() / (len(prices) - 2)
+            se_b = np.sqrt(mse / (len(prices) * var))
+
+        return {'value': b, 'std_error': se_b}
+
+    def _arc_elasticity(self, prices, quantities):
+        """
+        Arc elasticity: average of period-over-period elasticities.
+        E_t = (ΔQ/Q_avg) / (ΔP/P_avg)
+        """
+        elasticities = []
+
+        for i in range(1, len(prices)):
+            p1, p2 = prices[i-1], prices[i]
+            q1, q2 = quantities[i-1], quantities[i]
+
+            p_avg = (p1 + p2) / 2
+            q_avg = (q1 + q2) / 2
+
+            if p_avg == 0 or q_avg == 0:
+                continue
+
+            delta_p = p2 - p1
+            delta_q = q2 - q1
+
+            if delta_p == 0:
+                continue
+
+            e = (delta_q / q_avg) / (delta_p / p_avg)
+            elasticities.append(e)
+
+        if not elasticities:
+            return None
+
+        return {
+            'value': np.mean(elasticities),
+            'std_error': np.std(elasticities) / np.sqrt(len(elasticities))
+        }
+
+
+def aggregate_price_logs(price_logs: pd.DataFrame,
+                         window_size: str = '1H',
+                         ts_col: str = 'ts',
+                         store_mode : str = 'hotel') -> List[Dict]:
+    """
+    Recover price vectors treating prices as persistent state changes.
+
+    Prices are set-operations that persist until next change. For each window:
+    - If price logs exist: average all changes within window
+    - If no logs: carry forward last price before window end
+
+    Args:
+        price_logs: df with [productId, price, ts, ...]
+        window_size: time window size matching ChunkInteractionsIntoSteps
+        ts_col: timestamp column name
+
+    Returns:
+        list of dicts with {'window_start', 'window_end', 'price_vector'}
+        where price_vector is df with [productId, price]
+    """
+    if price_logs.empty:
+        return []
+
+    df = price_logs.copy()
+
+    if not pd.api.types.is_datetime64_any_dtype(df[ts_col]):
+        df[ts_col] = pd.to_datetime(df[ts_col])
+
+    df = df.sort_values([ts_col, 'productId'])
+    all_products=supabase.table(f'{store_mode}_products').select("id, room_type, date_index, metadata, availability").execute()
+    all_products = pd.DataFrame(all_products.data)
+    unique_products = all_products['id'].unique()
+
+    # generate windows across data range
+    min_time, max_time = df[ts_col].min(), df[ts_col].max()
+    windows = pd.date_range(
+        start=min_time.floor(window_size),
+        end=max_time,
+        freq=window_size
+    )
+
+    chunks = []
+
+    for window_start in windows:
+        window_end = window_start + pd.Timedelta(window_size)
+        price_vector = []
+
+        # all products with price history by window_end
+        #historical_products = df[df[ts_col] < window_end]['productId'].unique()
+        historical_products = unique_products.tolist()
+
+        for pid in historical_products:
+            product_data = df[df['productId'] == pid]
+
+            # logs within window
+            in_window = product_data[
+                (product_data[ts_col] >= window_start) &
+                (product_data[ts_col] < window_end)
+            ]
+
+            if not in_window.empty:
+                # average changes within window
+                price = in_window['price'].mean()
+            else:
+                # carry forward: last price before window end
+                before_window = product_data[product_data[ts_col] < window_end]
+                if before_window.empty:
+                    continue
+                price = before_window['price'].iloc[-1]
+
+            price_vector.append({'productId': pid, 'price': price})
+
+        if price_vector:
+            chunks.append({
+                'window_start': window_start,
+                'window_end': window_end,
+                'price_vector': pd.DataFrame(price_vector)
+            })
+
+    return chunks

experiments/procesing/metrics.py

@@ -0,0 +1,245 @@
+"""
+Revenue and KPI benchmark framework for pricing strategies.
+
+Computes session-level and aggregate metrics to compare pricing functions:
+    - Revenue: R_T = Σ P_t^T · Q_t
+    - Conversion rate
+    - Average order value (AOV)
+    - Agent exploitation loss: L_agent = R_oracle - R_observed
+"""
+from typing import Dict, List, Any, Optional
+from dataclasses import dataclass, field, asdict
+import pandas as pd
+import numpy as np
+
+
+@dataclass
+class SessionMetrics:
+    """KPIs for single session."""
+    session_id: str
+    experiment_id: Optional[str] = None
+
+    # interaction metrics
+    total_interactions: int = 0
+    page_views: int = 0
+    item_views: int = 0
+    searches: int = 0
+    cart_adds: int = 0
+
+    # revenue metrics
+    items_purchased: int = 0
+    total_revenue: float = 0.0
+    avg_item_price: float = 0.0
+    conversion_rate: float = 0.0
+
+    # pricing signals
+    total_price_shown: float = 0.0  # sum of all prices displayed
+    avg_markup: float = 0.0  # avg (price / base_price)
+
+    # behavioral features (for agent detection)
+    interaction_velocity: float = 0.0  # interactions per minute
+    session_duration_sec: float = 0.0
+    unique_products_viewed: int = 0
+
+    metadata: Dict[str, Any] = field(default_factory=dict)
+
+    def to_dict(self) -> Dict[str, Any]:
+        return asdict(self)
+
+
+@dataclass
+class AggregateMetrics:
+    """Aggregate KPIs across sessions/experiments."""
+    experiment_id: Optional[str] = None
+    n_sessions: int = 0
+
+    # revenue aggregates
+    total_revenue: float = 0.0
+    avg_revenue_per_session: float = 0.0
+    median_revenue_per_session: float = 0.0
+
+    # conversion aggregates
+    total_conversions: int = 0
+    conversion_rate: float = 0.0  # purchases / sessions
+
+    # pricing aggregates
+    avg_markup: float = 0.0
+    median_markup: float = 0.0
+
+    # agent exploitation metrics
+    estimated_agent_sessions: int = 0  # sessions flagged as agent-driven
+    agent_revenue: float = 0.0
+    human_revenue: float = 0.0
+    agent_loss: float = 0.0  # L_agent = R_oracle - R_observed (if available)
+
+    def to_dict(self) -> Dict[str, Any]:
+        return asdict(self)
+
+
+class MetricsComputer:
+    """Compute session and aggregate metrics from interaction/price logs."""
+
+    @staticmethod
+    def compute_session_metrics(
+        session_id: str,
+        interactions: pd.DataFrame,
+        price_logs: pd.DataFrame,
+        purchases: Optional[pd.DataFrame] = None,
+        experiment_id: Optional[str] = None
+    ) -> SessionMetrics:
+        """
+        Compute metrics for single session.
+
+        Args:
+            session_id: session identifier
+            interactions: user-interactions events for this session
+            price_logs: price-logs for this session
+            purchases: purchase events (if available)
+            experiment_id: experiment identifier
+        """
+        metrics = SessionMetrics(session_id=session_id, experiment_id=experiment_id)
+
+        if interactions.empty:
+            return metrics
+
+        # interaction counts
+        event_counts = interactions['eventName'].value_counts().to_dict()
+        metrics.total_interactions = len(interactions)
+        metrics.page_views = event_counts.get('page_view', 0) + event_counts.get('view_item_page', 0)
+        metrics.item_views = event_counts.get('view_item_page', 0)
+        metrics.searches = event_counts.get('search', 0)
+        metrics.cart_adds = event_counts.get('add_item_to_cart', 0)
+
+        # unique products viewed
+        metrics.unique_products_viewed = interactions['productId'].dropna().nunique()
+
+        # session duration
+        if 'ts' in interactions.columns:
+            timestamps = pd.to_datetime(interactions['ts'])
+            metrics.session_duration_sec = (timestamps.max() - timestamps.min()).total_seconds()
+            if metrics.session_duration_sec > 0:
+                metrics.interaction_velocity = (metrics.total_interactions / metrics.session_duration_sec) * 60
+
+        # revenue from purchases
+        if purchases is not None and not purchases.empty:
+            metrics.items_purchased = len(purchases)
+            metrics.total_revenue = purchases['price'].sum() if 'price' in purchases.columns else 0.0
+            metrics.avg_item_price = metrics.total_revenue / metrics.items_purchased if metrics.items_purchased > 0 else 0.0
+            metrics.conversion_rate = 1.0 if metrics.items_purchased > 0 else 0.0
+
+        # pricing metrics
+        if not price_logs.empty:
+            metrics.total_price_shown = price_logs['price'].sum()
+            # compute markup if base_price available in price logs or join with product catalog
+            if 'base_price' in price_logs.columns:
+                valid_markup = price_logs[price_logs['base_price'] > 0]
+                if not valid_markup.empty:
+                    metrics.avg_markup = (valid_markup['price'] / valid_markup['base_price']).mean()
+
+        return metrics
+
+    @staticmethod
+    def compute_aggregate_metrics(
+        session_metrics_list: List[SessionMetrics],
+        experiment_id: Optional[str] = None,
+        agent_detector_fn: Optional[callable] = None
+    ) -> AggregateMetrics:
+        """
+        Aggregate metrics across sessions.
+
+        Args:
+            session_metrics_list: list of SessionMetrics
+            experiment_id: experiment identifier
+            agent_detector_fn: optional function to classify session as agent (returns bool)
+        """
+        agg = AggregateMetrics(experiment_id=experiment_id)
+        agg.n_sessions = len(session_metrics_list)
+
+        if agg.n_sessions == 0:
+            return agg
+
+        df = pd.DataFrame([m.to_dict() for m in session_metrics_list])
+
+        # revenue aggregates
+        agg.total_revenue = df['total_revenue'].sum()
+        agg.avg_revenue_per_session = df['total_revenue'].mean()
+        agg.median_revenue_per_session = df['total_revenue'].median()
+
+        # conversion aggregates
+        agg.total_conversions = (df['items_purchased'] > 0).sum()
+        agg.conversion_rate = agg.total_conversions / agg.n_sessions
+
+        # pricing aggregates
+        valid_markups = df[df['avg_markup'] > 0]
+        if not valid_markups.empty:
+            agg.avg_markup = valid_markups['avg_markup'].mean()
+            agg.median_markup = valid_markups['avg_markup'].median()
+
+        # agent detection (if detector provided)
+        if agent_detector_fn is not None:
+            agent_flags = [agent_detector_fn(m) for m in session_metrics_list]
+            agg.estimated_agent_sessions = sum(agent_flags)
+
+            agent_revenue = sum(m.total_revenue for m, is_agent in zip(session_metrics_list, agent_flags) if is_agent)
+            human_revenue = sum(m.total_revenue for m, is_agent in zip(session_metrics_list, agent_flags) if not is_agent)
+
+            agg.agent_revenue = agent_revenue
+            agg.human_revenue = human_revenue
+
+        return agg
+
+    @staticmethod
+    def compare_pricing_strategies(
+        experiments: Dict[str, List[SessionMetrics]],
+        baseline_experiment_id: Optional[str] = None
+    ) -> pd.DataFrame:
+        """
+        Compare multiple pricing strategies/experiments.
+
+        Args:
+            experiments: dict mapping experiment_id -> list of SessionMetrics
+            baseline_experiment_id: experiment to use as baseline for comparison
+
+        Returns:
+            DataFrame with comparative metrics
+        """
+        results = []
+        baseline_agg = None
+
+        for exp_id, session_metrics in experiments.items():
+            agg = MetricsComputer.compute_aggregate_metrics(session_metrics, experiment_id=exp_id)
+            result = agg.to_dict()
+
+            if exp_id == baseline_experiment_id:
+                baseline_agg = agg
+
+            results.append(result)
+
+        df = pd.DataFrame(results)
+
+        # add relative metrics if baseline exists
+        if baseline_agg is not None:
+            df['revenue_lift_pct'] = ((df['total_revenue'] - baseline_agg.total_revenue) / baseline_agg.total_revenue * 100)
+            df['conversion_lift_pct'] = ((df['conversion_rate'] - baseline_agg.conversion_rate) / baseline_agg.conversion_rate * 100)
+
+        return df
+
+
+def simple_agent_detector(session_metrics: SessionMetrics, velocity_threshold: float = 5.0) -> bool:
+    """
+    Simple heuristic agent detector based on interaction velocity.
+
+    Args:
+        session_metrics: SessionMetrics instance
+        velocity_threshold: interactions per minute threshold (default: 5.0)
+
+    Returns:
+        True if session likely agent-driven
+    """
+    # agents tend to have higher interaction velocity and lower session duration
+    if session_metrics.interaction_velocity > velocity_threshold:
+        return True
+    # agents often view many products quickly without converting
+    if session_metrics.unique_products_viewed > 10 and session_metrics.conversion_rate == 0:
+        return True
+    return False

experiments/procesing/pipelines.py

@@ -0,0 +1,138 @@
+from sklearn.pipeline import Pipeline
+import pandas as pd
+from procesing.context import PipelineContext
+from procesing.providers import SupabaseProvider, BackendAPIProvider
+from typing import Union
+from procesing.steps import (
+    FetchInteractionsStep,
+    FetchPriceLogsStep,
+    FetchExperimentsStep,
+    JoinExperimentsStep,
+    CreatePriceBucketsStep,
+    AugmentEventNamesStep,
+    ChunkByTimeWindowStep,
+    ComputeDemandForChunksStep,
+    AggregatePriceLogsStep,
+    ComputeElasticityStep,
+    BuildStateSpaceStep,
+    FitPricingFunctionStep,
+    PredictPricesStep,
+)
+
+def interaction_extraction_pipeline(context: PipelineContext):
+    """Pipeline for extracting and augmenting interaction data"""
+    return Pipeline([
+        ('fetch', FetchInteractionsStep(context)),
+        ('create_buckets', CreatePriceBucketsStep(context)),
+        ('augment_events', AugmentEventNamesStep(context)),
+    ])
+
+
+def price_extraction_pipeline(context: PipelineContext):
+    """Pipeline for extracting price logs"""
+    return Pipeline([
+        ('fetch', FetchPriceLogsStep(context)),
+    ])
+
+
+def elasticity_computation_pipeline(context: PipelineContext,
+                                    interactions_df: pd.DataFrame,
+                                    price_logs_df: pd.DataFrame):
+    """
+    Compute elasticity from interactions and price logs.
+    Manual orchestration needed for branching logic.
+    """
+    # branch 1: chunk interactions and compute demand
+    chunk_step = ChunkByTimeWindowStep(context)
+    interaction_chunks = chunk_step.transform(interactions_df)
+
+    demand_step = ComputeDemandForChunksStep(context)
+    demand_chunks = demand_step.transform(interaction_chunks)
+
+    # branch 2: aggregate price logs
+    price_step = AggregatePriceLogsStep(context)
+    price_chunks = price_step.transform(price_logs_df)
+
+    # convergence: compute elasticity
+    elasticity_step = ComputeElasticityStep(context)
+    elasticity_df = elasticity_step.transform((demand_chunks, price_chunks))
+
+    return elasticity_df
+
+
+def pricing_pipeline(context: PipelineContext, elasticity_df: pd.DataFrame):
+    """
+    Generate optimal prices from elasticity estimates.
+    """
+    # build state space
+    state_step = BuildStateSpaceStep(context)
+    state_space = state_step.transform(elasticity_df)
+
+    # fit pricing function
+    fit_step = FitPricingFunctionStep(context)
+    pricer = fit_step.transform(elasticity_df)
+
+    # predict prices
+    predict_step = PredictPricesStep(context)
+    prices_df = predict_step.transform((pricer, state_space))
+
+    return prices_df
+
+
+def full_pipeline(context: PipelineContext):
+    """
+    Complete end-to-end pipeline: data extraction -> elasticity -> pricing
+    Returns: (elasticity_df, prices_df)
+    """
+    # extract interactions
+    interaction_pipe = interaction_extraction_pipeline(context)
+    interactions_df = interaction_pipe.fit_transform(None)
+
+    # extract price logs
+    price_pipe = price_extraction_pipeline(context)
+    price_logs_df = price_pipe.fit_transform(None)
+
+    if interactions_df.empty or price_logs_df.empty:
+        return None, None
+
+    # compute elasticity
+    elasticity_df = elasticity_computation_pipeline(
+        context,
+        interactions_df,
+        price_logs_df
+    )
+
+    if elasticity_df is None or elasticity_df.empty:
+        return elasticity_df, None
+
+    # generate prices
+    prices_df = pricing_pipeline(context, elasticity_df)
+
+    return elasticity_df, prices_df
+
+
+if __name__ == '__main__':
+
+    class Provider(SupabaseProvider, BackendAPIProvider):
+        def __init__(self, backend_url: str):
+            SupabaseProvider.__init__(self)
+            BackendAPIProvider.__init__(self, backend_url=backend_url)
+    # example run
+    context = PipelineContext(
+        provider=Provider(backend_url="http://localhost:5000"),
+        store_mode='hotel',
+    )
+
+    elasticity_df, prices_df = full_pipeline(context)
+
+    if elasticity_df is not None and not elasticity_df.empty:
+        print("Elasticity Estimates:")
+        print(elasticity_df.to_string(index=False))
+    else:
+        print("No elasticity estimates computed.")
+
+    if prices_df is not None and not prices_df.empty:
+        print("\nPredicted Prices:")
+        print(prices_df.to_string(index=False))
+    else:
+        print("No prices predicted.")

experiments/procesing/pricers/__init__.py

@@ -0,0 +1,13 @@
+from procesing.pricers.base import PricingFunction
+from procesing.pricers.elasticity import ElasticityBasedPricer
+from procesing.pricers.simple import StaticPricer, RandomPricer
+from procesing.pricers.session_aware import SessionAwarePricer, ProductSpecificSessionPricer
+
+__all__ = [
+    'PricingFunction',
+    'ElasticityBasedPricer',
+    'StaticPricer',
+    'RandomPricer',
+    'SessionAwarePricer',
+    'ProductSpecificSessionPricer'
+]

experiments/procesing/pricers/base.py

@@ -0,0 +1,70 @@
+from abc import ABC, abstractmethod
+from typing import Optional, Dict, Any, List
+import numpy as np
+import pandas as pd
+
+
+class PricingFunction(ABC):
+    """
+    Abstract base for pricing functions.
+
+    Defines mapping: f(Q_t, P_t, S_t, H_t) -> P_{t+1}
+
+    Where:
+        Q_t ∈ R^n: demand vector at time t
+        P_t ∈ R^n: price vector at time t
+        S_t: session features (behavioral signals, interactions)
+        H_t = {Q_{t-k}, P_{t-k}, S_{t-k}}: historical state trajectory
+
+    Objective:
+        maximize E[R_T] = E[Σ P_t^T · Q_t]
+        subject to:
+            Q_t = g(P_t, S_t)  (demand response via elasticity)
+            P_t ≥ C  (cost floor)
+            minimize L_agent = R_oracle - R_observed
+    """
+
+    @abstractmethod
+    def fit(self, historical_data: pd.DataFrame, **kwargs):
+        """
+        Offline training on historical data.
+
+        Args:
+            historical_data: DataFrame with elasticity, prices, demand signals
+            **kwargs: additional training parameters
+        """
+        pass
+
+    @abstractmethod
+    def predict(self, state_space) -> np.ndarray:
+        """
+        Generate optimal prices given current state.
+
+        Args:
+            state_space: StateSpace object containing Q_t, P_t, S_t, H_t
+
+        Returns:
+            P_{t+1}: price vector in R^n
+        """
+        pass
+
+    def update(self, observation: Dict[str, Any]):
+        """
+        Online learning update (optional).
+
+        Args:
+            observation: dict with {state, action, reward, next_state}
+                - state: StateSpace before pricing decision
+                - action: prices shown (P_t)
+                - reward: revenue/conversion signal
+                - next_state: StateSpace after user interaction
+        """
+        pass  # default: no online learning
+
+    def get_params(self) -> Dict[str, Any]:
+        """Return pricing function parameters for serialization."""
+        return {}
+
+    def set_params(self, params: Dict[str, Any]):
+        """Load pricing function parameters from dict."""
+        pass

experiments/procesing/pricers/elasticity.py

@@ -0,0 +1,59 @@
+import numpy as np
+import pandas as pd
+from procesing.pricers.base import PricingFunction
+
+
+class ElasticityBasedPricer(PricingFunction):
+    """
+    Pricing based on demand elasticity estimates.
+    f(Q, S) = base_price * (1 + alpha * elasticity * demand_deviation)
+    """
+
+    def __init__(self, alpha: float = 0.1, price_floor: float = 0.0, price_ceil: float = np.inf):
+        self.alpha = alpha
+        self.price_floor = price_floor
+        self.price_ceil = price_ceil
+        self.elasticity = None
+        self.base_prices = None
+        self.mean_demand = None
+
+    def fit(self, historical_data: pd.DataFrame):
+        """
+        Calibrate from historical elasticity estimates.
+        Expects: [productId, elasticity, base_price, mean_demand]
+        """
+        if 'elasticity' not in historical_data.columns:
+            raise ValueError("historical_data must contain 'elasticity' column")
+
+        self.elasticity = historical_data['elasticity'].values
+        self.base_prices = (historical_data['base_price'].values
+                           if 'base_price' in historical_data.columns
+                           else np.ones(len(historical_data)) * 100)
+        self.mean_demand = (historical_data['mean_demand'].values
+                           if 'mean_demand' in historical_data.columns
+                           else np.ones(len(historical_data)) * 10)
+        return self
+
+    def predict(self, state_space) -> np.ndarray:
+        """
+        Adjust prices based on demand deviation and elasticity.
+        Higher demand -> increase price (but less for elastic goods)
+        """
+        if self.elasticity is None:
+            raise ValueError("Must call fit() before predict()")
+
+        demand = np.asarray(state_space.demand)
+        if len(demand) != len(self.elasticity):
+            raise ValueError(f"Demand vector size {len(demand)} != elasticity size {len(self.elasticity)}")
+
+        # compute demand deviation from mean
+        demand_dev = (demand - self.mean_demand) / (self.mean_demand + 1e-6)
+
+        # adjust price: if demand high and elastic, don't increase much
+        # if demand high and inelastic, increase more
+        price_multiplier = 1 + self.alpha * np.abs(self.elasticity) * demand_dev
+        prices = self.base_prices * price_multiplier
+
+        # enforce bounds
+        prices = np.clip(prices, self.price_floor, self.price_ceil)
+        return prices

experiments/procesing/pricers/session_aware.py

@@ -0,0 +1,172 @@
+"""
+Session-aware pricing functions that leverage behavioral features S_t.
+These pricers aim to minimize L_agent = R_oracle - R_observed.
+"""
+import numpy as np
+import pandas as pd
+from procesing.pricers.base import PricingFunction
+from procesing.pricers.elasticity import ElasticityBasedPricer
+
+
+class SessionAwarePricer(PricingFunction):
+    """
+    Extends elasticity-based pricing with session behavioral signals.
+
+    f(Q, P, S) = base_price * elasticity_factor * session_factor
+
+    Where session_factor adjusts for:
+        - interaction_velocity (agent detection proxy)
+        - product_view_depth (interest signal)
+        - cart_to_view_ratio (conversion intent)
+
+    Strategy: charge higher prices to suspected agents (high velocity)
+    to recover oracle revenue from reconnaissance sessions.
+    """
+
+    def __init__(self,
+                 alpha: float = 0.1,
+                 beta_velocity: float = 0.05,
+                 beta_attention: float = 0.03,
+                 agent_velocity_threshold: float = 5.0,
+                 agent_markup: float = 1.2,
+                 price_floor: float = 0.0,
+                 price_ceil: float = np.inf):
+        """
+        Args:
+            alpha: elasticity sensitivity
+            beta_velocity: interaction velocity weight
+            beta_attention: product attention weight
+            agent_velocity_threshold: velocity above which to apply agent markup
+            agent_markup: price multiplier for suspected agent sessions
+            price_floor, price_ceil: price bounds
+        """
+        self.alpha = alpha
+        self.beta_velocity = beta_velocity
+        self.beta_attention = beta_attention
+        self.agent_velocity_threshold = agent_velocity_threshold
+        self.agent_markup = agent_markup
+        self.price_floor = price_floor
+        self.price_ceil = price_ceil
+
+        # fitted parameters
+        self.elasticity = None
+        self.base_prices = None
+        self.mean_demand = None
+
+    def fit(self, historical_data: pd.DataFrame, **kwargs):
+        """Calibrate from historical elasticity data."""
+        if 'elasticity' not in historical_data.columns:
+            raise ValueError("historical_data must contain 'elasticity'")
+
+        self.elasticity = historical_data['elasticity'].values
+        self.base_prices = (historical_data['base_price'].values
+                           if 'base_price' in historical_data.columns
+                           else np.ones(len(historical_data)) * 100)
+        self.mean_demand = (historical_data['mean_demand'].values
+                           if 'mean_demand' in historical_data.columns
+                           else np.ones(len(historical_data)) * 10)
+        return self
+
+    def predict(self, state_space) -> np.ndarray:
+        """Generate prices with session awareness."""
+        if self.elasticity is None:
+            raise ValueError("Must call fit() before predict()")
+
+        demand = np.asarray(state_space.demand)
+        n_products = len(demand)
+
+        # base elasticity-driven pricing
+        demand_dev = (demand - self.mean_demand) / (self.mean_demand + 1e-6)
+        elasticity_factor = 1 + self.alpha * np.abs(self.elasticity) * demand_dev
+
+        # session-aware adjustments
+        session_factor = np.ones(n_products)
+
+        if not state_space.session_features.empty:
+            sf = state_space.session_features.iloc[0]  # single session features
+
+            # agent detection via velocity
+            velocity = sf.get('interaction_velocity', 0.0)
+            if velocity > self.agent_velocity_threshold:
+                # suspected agent: apply markup to recover oracle revenue
+                session_factor *= self.agent_markup
+
+            # attention signal: higher view depth -> user interested -> can charge more
+            view_depth = sf.get('product_view_depth', 0)
+            if view_depth > 0:
+                attention_boost = 1 + self.beta_attention * np.log1p(view_depth)
+                session_factor *= attention_boost
+
+            # cart presence: if user has items in cart, slightly increase prices
+            cart_to_view = sf.get('cart_to_view_ratio', 0.0)
+            if cart_to_view > 0.1:
+                session_factor *= (1 + 0.02)  # small boost for conversion intent
+
+        prices = self.base_prices * elasticity_factor * session_factor
+        prices = np.clip(prices, self.price_floor, self.price_ceil)
+
+        return prices
+
+
+class ProductSpecificSessionPricer(PricingFunction):
+    """
+    Session-aware pricer with product-specific demand signals.
+
+    Uses S_t to extract per-product interaction counts and adjusts pricing
+    for products the user has already viewed/hovered.
+
+    Strategy: products viewed multiple times = high interest -> price up
+    """
+
+    def __init__(self,
+                 alpha: float = 0.1,
+                 view_boost: float = 0.02,
+                 max_view_boost: float = 0.15,
+                 price_floor: float = 0.0,
+                 price_ceil: float = np.inf):
+        self.alpha = alpha
+        self.view_boost = view_boost
+        self.max_view_boost = max_view_boost
+        self.price_floor = price_floor
+        self.price_ceil = price_ceil
+
+        self.elasticity = None
+        self.base_prices = None
+        self.mean_demand = None
+        self.product_ids = None
+
+    def fit(self, historical_data: pd.DataFrame, **kwargs):
+        if 'elasticity' not in historical_data.columns or 'productId' not in historical_data.columns:
+            raise ValueError("historical_data must contain 'elasticity' and 'productId'")
+
+        self.elasticity = historical_data['elasticity'].values
+        self.base_prices = (historical_data['base_price'].values
+                           if 'base_price' in historical_data.columns
+                           else np.ones(len(historical_data)) * 100)
+        self.mean_demand = (historical_data['mean_demand'].values
+                           if 'mean_demand' in historical_data.columns
+                           else np.ones(len(historical_data)) * 10)
+        self.product_ids = historical_data['productId'].values
+        return self
+
+    def predict(self, state_space) -> np.ndarray:
+        if self.elasticity is None:
+            raise ValueError("Must call fit() before predict()")
+
+        demand = np.asarray(state_space.demand)
+        n_products = len(demand)
+
+        # base pricing
+        demand_dev = (demand - self.mean_demand) / (self.mean_demand + 1e-6)
+        base_prices = self.base_prices * (1 + self.alpha * np.abs(self.elasticity) * demand_dev)
+
+        # product-specific session adjustments
+        if not state_space.session_features.empty and state_space.product_ids is not None:
+            # extract product interaction counts from session metadata
+            # (this would require session features to include per-product signals)
+            # for now, use uniform boost as placeholder
+            # TODO: extend session feature extraction to include product-specific counts
+            pass
+
+        prices = np.clip(base_prices, self.price_floor, self.price_ceil)
+        return prices

experiments/procesing/pricers/simple.py

@@ -0,0 +1,48 @@
+import numpy as np
+import pandas as pd
+from procesing.pricers.base import PricingFunction
+
+
+class StaticPricer(PricingFunction):
+    """Static pricing: always return fixed base prices"""
+
+    def __init__(self, base_prices: np.ndarray = None):
+        self.base_prices = base_prices
+
+    def fit(self, historical_data: pd.DataFrame):
+        """Extract base prices from historical data"""
+        if 'base_price' in historical_data.columns:
+            self.base_prices = historical_data['base_price'].values
+        elif 'price' in historical_data.columns:
+            self.base_prices = historical_data['price'].values
+        else:
+            raise ValueError("historical_data must contain 'base_price' or 'price' column")
+        return self
+
+    def predict(self, state_space) -> np.ndarray:
+        """Return static base prices regardless of state"""
+        if self.base_prices is None:
+            raise ValueError("Must call fit() or provide base_prices in constructor")
+        return self.base_prices.copy()
+
+
+class RandomPricer(PricingFunction):
+    """Random pricing within bounds (for baseline comparison)"""
+
+    def __init__(self, price_min: float = 50.0, price_max: float = 500.0, seed: int = None):
+        self.price_min = price_min
+        self.price_max = price_max
+        self.seed = seed
+        self.n_products = None
+        self.rng = np.random.default_rng(seed)
+
+    def fit(self, historical_data: pd.DataFrame):
+        """Learn number of products"""
+        self.n_products = len(historical_data)
+        return self
+
+    def predict(self, state_space) -> np.ndarray:
+        """Generate random prices"""
+        if self.n_products is None:
+            self.n_products = len(state_space.demand)
+        return self.rng.uniform(self.price_min, self.price_max, size=self.n_products)

experiments/procesing/pricing.py

@@ -0,0 +1,272 @@
+r"""
+Our state space comes as:
+$Q_t in R^n$ - our demand at a time t
+$P_t in R^n$ - prices at time t
+$S_t$ some form of interaction session features
+
+This is a single sate which we map under
+
+$f: (Q, S, H) \to P_{t+1}$
+
+With:
+
+$H_t = \{Q_{t-k}, P_{t-k}, S_{t-k}\}$
+
+
+We can have f be literally anything, analytical or learned or rule based or an RL policy.
+
+Our goal is to mazimize the expected revenue:
+
+$E[R_T] = E[\sum_{t=1}^T P_t^T \dot Q_t]$
+
+subject to Q_t = g(P_t, S_t) : demand response to price (estimated via elasticity) and P_t ≥ C : prices above cost floor and additionally minimizing the following:
+
+$L_{agent} = R_{oracle} - R_{observed}
+
+where: R_oracle = revenue if we knew agent intentions (from recon session) and R_observed = revenue under current pricing policy f
+
+I would start be defning a pricing function interface and standardizing how to train that based on historical data and define how to make it behave for online training (if we do that)
+
+We also need to develop a solid benchmark with mapping revenue and full KPIs from session interactions to measure differences between different price learning methods
+"""
+
+from abc import ABC, abstractmethod
+from sklearn.base import BaseEstimator, TransformerMixin
+import numpy as np
+import pandas as pd
+import os
+from dotenv import load_dotenv
+load_dotenv()
+from supabase import create_client, Client
+
+SUPABASE_URL = os.getenv("NEXT_PUBLIC_SUPABASE_URL", "")
+SUPABASE_KEY = os.getenv("NEXT_PUBLIC_SUPABASE_ANON_KEY", "")
+supabase: Client = create_client(SUPABASE_URL, SUPABASE_KEY)
+
+def expected_revenue(prices: np.ndarray, demand: np.ndarray) -> float:
+    """Returns: expected revenue R_t = P_t^T * Q_t"""
+    return float(np.dot(prices, demand))
+
+class StateSpace:
+    def __init__(self,
+                 demand : np.ndarray, # at time t, only values (assuming aligned by productId order)
+                 prices : np.ndarray, # at time t, only values (assuming aligned by productId order)
+                 session_features : pd.DataFrame):
+        self.demand = demand  # Q_t
+        self.prices = prices  # P_t
+        self.session_features = session_features  # S_t
+        self.history = []  # H_t
+
+class PricingFunction(BaseEstimator, TransformerMixin, ABC):
+    def __init__(self):
+        pass
+
+    def fit(self, historical_data):
+        """
+        Train the pricing function based on historical data.
+        historical_data: list of StateSpace instances with known outcomes
+        """
+        raise NotImplementedError("Train method must be implemented by subclass.")
+
+    def transform(self, state_space) -> np.ndarray:
+        """
+        Predict the next prices given the current state space.
+        state_space: StateSpace instance
+        Returns: predicted prices P_{t+1}
+        """
+        raise NotImplementedError("Predict method must be implemented by subclass.")
+
+
+class SimpleLinearPricingFunction(PricingFunction):
+    def __init__(self, price_sensitivity: float = -0.1):
+        super().__init__()
+        self.price_sensitivity = price_sensitivity
+
+    def fit(self, historical_data):
+        return self
+
+    def transform(self, state_space: StateSpace) -> np.ndarray:
+        new_prices = state_space.prices + self.price_sensitivity * state_space.demand
+        return np.maximum(new_prices, 0)
+
+
+class ElasticityBasedPricingFunction(PricingFunction):
+    """
+    Revenue-maximizing pricing using elasticity estimates.
+
+    For each product, optimal price P* maximizes R = P * Q(P)
+    where Q(P) follows power law: Q(P) = Q_0 * (P/P_0)^ε
+
+    Taking derivative dR/dP = 0 gives optimal markup:
+    P* = P_0 * (1 + 1/ε) if ε < -1 (elastic)
+
+    For inelastic demand (|ε| < 1), we apply bounded markup.
+    """
+
+    def __init__(self,
+                 cost_floor: float = 0.5,
+                 max_markup: float = 2.0,
+                 min_markup: float = 1.0,
+                 inelastic_markup: float = 1.3):
+        super().__init__()
+        self.cost_floor = cost_floor  # prices as fraction of base
+        self.max_markup = max_markup  # max price = base * max_markup
+        self.min_markup = min_markup  # min price = base * min_markup
+        self.inelastic_markup = inelastic_markup  # default for |ε| < 1
+        self.elasticity_map = {}  # productId -> elasticity
+
+    def fit(self, elasticity_df: pd.DataFrame):
+        """
+        Args:
+            elasticity_df: df with [productId, elasticity, std_error, n_obs]
+        """
+        if elasticity_df is not None and not elasticity_df.empty:
+            self.elasticity_map = dict(zip(
+                elasticity_df['productId'],
+                elasticity_df['elasticity']
+            ))
+        return self
+
+    def transform(self, state_space: StateSpace, product_ids: np.ndarray = None) -> np.ndarray:
+        """
+        Args:
+            state_space: current state (prices = base prices)
+            product_ids: array of productIds aligned with state_space.prices
+
+        Returns:
+            optimized prices P_{t+1}
+        """
+        base_prices = state_space.prices
+
+        if product_ids is None:
+            # fallback: use positional index as productId (not ideal)
+            product_ids = np.arange(len(base_prices))
+
+        new_prices = np.zeros_like(base_prices)
+
+        for i, (base_p, pid) in enumerate(zip(base_prices, product_ids)):
+            elasticity = self.elasticity_map.get(pid, 0.0)
+
+            if elasticity < -1:  # elastic demand
+                # optimal markup: (1 + 1/ε)
+                markup = 1 + (1 / elasticity)
+                optimal_p = base_p * markup
+            elif elasticity > -1 and elasticity < 0:  # inelastic
+                # conservative markup
+                optimal_p = base_p * self.inelastic_markup
+            else:  # ε ≥ 0 (demand increases with price, or no data)
+                # no elasticity data or anomalous, keep base price
+                optimal_p = base_p
+
+            # apply bounds
+            optimal_p = np.clip(
+                optimal_p,
+                base_p * self.min_markup,
+                base_p * self.max_markup
+            )
+            optimal_p = max(optimal_p, self.cost_floor)
+
+            new_prices[i] = optimal_p
+
+        return new_prices
+
+
+class ContextualElasticityPricing(PricingFunction):
+    """
+    Revenue optimization with contextual adjustments based on session features.
+
+    Combines elasticity-based pricing with surge/demand-based multipliers.
+    """
+
+    def __init__(self,
+                 base_pricer: ElasticityBasedPricingFunction = None,
+                 demand_sensitivity: float = 0.1,
+                 surge_threshold: float = 0.7):
+        super().__init__()
+        self.base_pricer = base_pricer or ElasticityBasedPricingFunction()
+        self.demand_sensitivity = demand_sensitivity
+        self.surge_threshold = surge_threshold
+
+    def fit(self, elasticity_df: pd.DataFrame):
+        self.base_pricer.fit(elasticity_df)
+        return self
+
+    def transform(self, state_space: StateSpace, product_ids: np.ndarray = None) -> np.ndarray:
+        # get base optimal prices from elasticity
+        base_optimal = self.base_pricer.transform(state_space, product_ids)
+
+        # compute surge multiplier from demand
+        if len(state_space.demand) > 0:
+            demand_normalized = state_space.demand / (state_space.demand.max() + 1e-8)
+            surge_multiplier = 1 + self.demand_sensitivity * np.maximum(
+                demand_normalized - self.surge_threshold, 0
+            )
+        else:
+            surge_multiplier = np.ones_like(base_optimal)
+
+        return base_optimal * surge_multiplier
+
+# Example usage:
+if __name__ == "__main__":
+    from pipeline import interaction_pipeline, price_data_pipeline, elasticity_pipeline
+
+    store_mode = 'hotel'
+    interaction_data = interaction_pipeline.fit_transform(None)
+    price_data = price_data_pipeline.fit_transform(None)
+
+    elasticity_df = elasticity_pipeline(interaction_data, price_data, window_size="30s", store_mode=store_mode)
+
+    # fetch all products with base prices from database
+    products_resp = supabase.table(f'{store_mode}_products').select("id, metadata").execute()
+    products_df = pd.DataFrame(products_resp.data)
+
+    # extract base_price from metadata
+    products_df['base_price'] = products_df['metadata'].apply(lambda m: m.get('base_price', 0) if isinstance(m, dict) else 0)
+    products_df = products_df.rename(columns={'id': 'productId'})[['productId', 'base_price']]
+
+    # override with logged prices where available
+    if not price_data.empty:
+        if 'ts' in price_data.columns and not pd.api.types.is_datetime64_any_dtype(price_data['ts']):
+            price_data['ts'] = pd.to_datetime(price_data['ts'])
+
+        # get latest logged price per product
+        price_logs_agg = price_data.sort_values('ts').groupby('productId', as_index=False).last()
+
+        # merge: start with all products (base prices), override with logged prices
+        products_df = products_df.merge(
+            price_logs_agg[['productId', 'price']],
+            on='productId',
+            how='left'
+        )
+        products_df['final_price'] = products_df['price'].fillna(products_df['base_price'])
+    else:
+        products_df['final_price'] = products_df['base_price']
+
+    # merge with elasticity
+    if elasticity_df is not None and not elasticity_df.empty:
+        price_data_merged = products_df[['productId', 'final_price']].merge(
+            elasticity_df[['productId', 'elasticity']],
+            on='productId',
+            how='left'
+        ).fillna({'elasticity': 0.0})
+
+        prices = price_data_merged['final_price'].values
+        elasticities = price_data_merged['elasticity'].values
+    else:
+        prices = np.array([])
+        elasticities = np.array([])
+
+    print(elasticities)
+    print(prices)
+
+    state_space = StateSpace(
+        demand=elasticities,
+        prices=prices,
+        session_features=interaction_data
+    )
+
+    pricing_function = SimpleLinearPricingFunction(price_sensitivity=-0.05)
+    pricing_function.fit([])  # No training data for simple model
+    predicted_prices = pricing_function.transform(state_space)
+
+    print("Predicted Prices:", predicted_prices)

experiments/procesing/providers/__init__.py

@@ -0,0 +1,5 @@
+from procesing.providers.base import DataProvider
+from procesing.providers.supabase import SupabaseProvider
+from procesing.providers.backend import BackendAPIProvider
+
+__all__ = ['DataProvider', 'SupabaseProvider', 'BackendAPIProvider']

experiments/procesing/providers/backend.py

@@ -0,0 +1,19 @@
+import os
+import pandas as pd
+import requests
+from typing import List
+from procesing.providers.base import DataProvider
+
+class BackendAPIProvider(DataProvider):
+    """Concrete backend API implementation"""
+    def __init__(self, backend_url: str = None):
+        self.backend_url = backend_url or os.getenv("BACKEND_URL", "http://localhost:5000")
+    def fetch_kafka_topic(self, topic: str) -> pd.DataFrame:
+        resp = requests.get(f"{self.backend_url}/api/kafka/dump?topic={topic}")
+        resp.raise_for_status()
+        data = resp.json()
+
+        if not data.get('success') or not data.get('data'):
+            return pd.DataFrame()
+
+        return pd.DataFrame(data['data'])

experiments/procesing/providers/base.py

@@ -0,0 +1,21 @@
+from abc import ABC, abstractmethod
+from typing import List
+import pandas as pd
+
+class DataProvider(ABC):
+    """Abstract interface for data access, enables DI and testing"""
+
+    @abstractmethod
+    def fetch_products(self, store_mode: str) -> pd.DataFrame:
+        """Fetch product catalog for given store mode"""
+        pass
+
+    @abstractmethod
+    def fetch_experiments(self, experiment_ids: List[str]) -> pd.DataFrame:
+        """Fetch experiment metadata for given IDs"""
+        pass
+
+    @abstractmethod
+    def fetch_kafka_topic(self, topic: str) -> pd.DataFrame:
+        """Fetch data from Kafka topic via backend API"""
+        pass

experiments/procesing/providers/supabase.py

@@ -0,0 +1,35 @@
+import os
+import pandas as pd
+import requests
+from typing import List
+from supabase import create_client, Client
+from procesing.providers.base import DataProvider
+from dotenv import load_dotenv
+
+class SupabaseProvider(DataProvider):
+    """Concrete Supabase + backend API implementation"""
+
+    def __init__(self,
+                 supabase_url: str = None,
+                 supabase_key: str = None,):
+        load_dotenv()
+        self.supabase_url = supabase_url or os.getenv("NEXT_PUBLIC_SUPABASE_URL")
+        self.supabase_key = supabase_key or os.getenv("NEXT_PUBLIC_SUPABASE_ANON_KEY")
+        self.supabase: Client = create_client(self.supabase_url, self.supabase_key)
+
+    def fetch_products(self, store_mode: str) -> pd.DataFrame:
+        resp = self.supabase.table(f'{store_mode}_products').select(
+            "id, room_type, date_index, metadata, availability"
+        ).execute()
+        return pd.DataFrame(resp.data) if resp.data else pd.DataFrame()
+
+    def fetch_experiments(self, experiment_ids: List[str]) -> pd.DataFrame:
+        if not experiment_ids:
+            return pd.DataFrame()
+
+        resp = self.supabase.table('experiments').select(
+            'id, subject_name, xp_human_only, xp_market_mode, xp_task_id, '
+            'task:tasks(task_name, task_description, task_def_of_done)'
+        ).in_('id', experiment_ids).execute()
+
+        return pd.DataFrame(resp.data) if resp.data else pd.DataFrame()

experiments/procesing/steps/__init__.py

@@ -0,0 +1,27 @@
+from procesing.steps.base import BaseContextStep
+from procesing.steps.fetch import FetchInteractionsStep, FetchPriceLogsStep, FetchExperimentsStep
+from procesing.steps.join import JoinExperimentsStep
+from procesing.steps.augment import CreatePriceBucketsStep, AugmentEventNamesStep
+from procesing.steps.chunk import ChunkByTimeWindowStep
+from procesing.steps.demand import ComputeDemandStep, ComputeDemandForChunksStep
+from procesing.steps.elasticity import AggregatePriceLogsStep, ComputeElasticityStep
+from procesing.steps.pricing import StateSpace, BuildStateSpaceStep, FitPricingFunctionStep, PredictPricesStep
+
+__all__ = [
+    'BaseContextStep',
+    'FetchInteractionsStep',
+    'FetchPriceLogsStep',
+    'FetchExperimentsStep',
+    'JoinExperimentsStep',
+    'CreatePriceBucketsStep',
+    'AugmentEventNamesStep',
+    'ChunkByTimeWindowStep',
+    'ComputeDemandStep',
+    'ComputeDemandForChunksStep',
+    'AggregatePriceLogsStep',
+    'ComputeElasticityStep',
+    'StateSpace',
+    'BuildStateSpaceStep',
+    'FitPricingFunctionStep',
+    'PredictPricesStep',
+]

experiments/procesing/steps/augment.py

@@ -0,0 +1,53 @@
+import numpy as np
+import pandas as pd
+from procesing.steps.base import BaseContextStep
+
+class CreatePriceBucketsStep(BaseContextStep):
+    """Create price bucket labels from price data"""
+
+    def transform(self, df: pd.DataFrame):
+        if df.empty or 'metadata_price' not in df.columns:
+            df['price_bucket'] = ""
+            return df
+
+        n_buckets = self.context.config.get('n_price_buckets', 5)
+
+        if df['metadata_price'].notnull().sum() > 0:
+            try:
+                price_buckets = pd.qcut(
+                    df['metadata_price'],
+                    q=n_buckets,
+                    labels=[f"PB_{i+1}" for i in range(n_buckets)],
+                    duplicates='drop'
+                )
+            except ValueError:
+                # fallback for insufficient unique values
+                price_buckets = df['metadata_price'].apply(
+                    lambda x: f"P_{int(x)}" if pd.notnull(x) else ""
+                )
+        else:
+            price_buckets = pd.Series([""] * len(df), index=df.index)
+
+        df['price_bucket'] = price_buckets
+        return df
+
+
+class AugmentEventNamesStep(BaseContextStep):
+    """Augment event names with product and price bucket schema"""
+
+    def transform(self, df: pd.DataFrame):
+        if df.empty:
+            return df
+
+        # Create schema: _productId@price_bucket
+        has_product = df.get('productId', pd.Series()).notnull()
+        has_bucket = df.get('price_bucket', pd.Series()).notnull()
+
+        df['metadata_schema'] = np.where(
+            has_product & has_bucket,
+            "_" + df['productId'].astype(str) + "@" + df['price_bucket'].astype(str),
+            ""
+        )
+
+        df['eventName'] = df['eventName'] + df['metadata_schema']
+        return df

experiments/procesing/steps/base.py

@@ -0,0 +1,31 @@
+from abc import ABC, abstractmethod
+from sklearn.base import BaseEstimator, TransformerMixin
+from procesing.context import PipelineContext
+
+class BaseContextStep(BaseEstimator, TransformerMixin, ABC):
+    """
+    Base for all pipeline steps.
+    Each step is stateless, context-driven, and performs ONE transformation.
+    """
+
+    def __init__(self, context: PipelineContext):
+        self.context = context
+
+    def fit(self, X=None, y=None):
+        """Most steps don't need training"""
+        return self
+
+    @abstractmethod
+    def transform(self, X):
+        """Transform input using context. Must be implemented by subclass."""
+        pass
+
+    def get_params(self, deep=True):
+        """sklearn compatibility"""
+        return {'context': self.context}
+
+    def set_params(self, **params):
+        """sklearn compatibility"""
+        if 'context' in params:
+            self.context = params['context']
+        return self

experiments/procesing/steps/chunk.py

@@ -0,0 +1,34 @@
+import pandas as pd
+from procesing.steps.base import BaseContextStep
+
+class ChunkByTimeWindowStep(BaseContextStep):
+    """
+    Chunk dataframe into time windows.
+    Returns list of dicts with window metadata.
+    """
+
+    def transform(self, df: pd.DataFrame):
+        if df.empty:
+            return []
+
+        df = df.copy()
+        ts_col = self.context.config.get('ts_col', 'ts')
+        window_size = self.context.window_size
+
+        # ensure datetime
+        if not pd.api.types.is_datetime64_any_dtype(df[ts_col]):
+            df[ts_col] = pd.to_datetime(df[ts_col])
+
+        df = df.sort_values(ts_col)
+        df['_window'] = df[ts_col].dt.floor(window_size)
+
+        chunks = []
+        for idx, (window_start, group) in enumerate(df.groupby('_window')):
+            chunks.append({
+                'window_start': window_start,
+                'window_end': window_start + pd.Timedelta(window_size),
+                'window_idx': idx,
+                'data': group.drop(columns=['_window'])
+            })
+
+        return chunks

experiments/procesing/steps/demand.py

@@ -0,0 +1,61 @@
+import pandas as pd
+from procesing.steps.base import BaseContextStep
+
+class ComputeDemandStep(BaseContextStep):
+    """
+    Compute demand vector for a single time window or dataframe.
+    Input: single chunk dict OR raw dataframe
+    Output: demand dataframe with [productId, demand_score]
+    """
+
+    def transform(self, chunk):
+        # handle both chunk dict and raw dataframe
+        if isinstance(chunk, dict):
+            interactions = chunk['data']
+            window_meta = {k: v for k, v in chunk.items() if k != 'data'}
+        else:
+            interactions = chunk
+            window_meta = {}
+
+        products = self.context.products
+        unique_products = products['id'].unique()
+
+        # apply filters if configured
+        session_filter = self.context.config.get('session_filter')
+        experiment_filter = self.context.config.get('experiment_filter')
+
+        if session_filter and 'sessionId' in interactions.columns:
+            interactions = interactions[interactions['sessionId'] == session_filter]
+        if experiment_filter and 'experimentId' in interactions.columns:
+            interactions = interactions[interactions['experimentId'] == experiment_filter]
+
+        interactions_with_products = interactions.dropna(subset=['productId'])
+
+        if interactions_with_products.empty:
+            demand_df = pd.DataFrame({
+                'productId': unique_products,
+                'demand_score': 0
+            })
+        else:
+            # crosstab for simple demand count
+            demand_df = pd.crosstab(
+                interactions_with_products['productId'],
+                'count'
+            ).reindex(unique_products, fill_value=0).reset_index()
+            demand_df.columns = ['productId', 'demand_score']
+
+        # attach window metadata if present
+        if window_meta:
+            return {**window_meta, 'demand_vector': demand_df}
+        return demand_df
+
+
+class ComputeDemandForChunksStep(BaseContextStep):
+    """Apply ComputeDemandStep to list of chunks"""
+
+    def transform(self, chunks: list):
+        if not chunks:
+            return []
+
+        demand_step = ComputeDemandStep(self.context)
+        return [demand_step.transform(chunk) for chunk in chunks]

experiments/procesing/steps/elasticity.py

@@ -0,0 +1,253 @@
+import numpy as np
+import pandas as pd
+from typing import Dict, List
+from procesing.steps.base import BaseContextStep
+
+class AggregatePriceLogsStep(BaseContextStep):
+    """
+    Aggregate price logs into time windows using VECTORIZED operations.
+    Input: price_logs_df
+    Output: list of price chunks with [productId, price]
+    """
+
+    def transform(self, price_logs_df: pd.DataFrame):
+        if price_logs_df.empty:
+            return []
+
+        df = price_logs_df.copy()
+        ts_col = self.context.config.get('ts_col', 'ts')
+        window_size = self.context.window_size
+
+        # ensure datetime
+        if not pd.api.types.is_datetime64_any_dtype(df[ts_col]):
+            df[ts_col] = pd.to_datetime(df[ts_col])
+
+        df = df.sort_values([ts_col, 'productId'])
+        products = self.context.products
+        unique_products = products['id'].unique()
+
+        # VECTORIZED: group by product, resample by time window, compute mean
+        df_indexed = df.set_index(ts_col)
+
+        windowed = (
+            df_indexed
+            .groupby('productId')['price']
+            .resample(window_size)
+            .mean()
+            .reset_index()
+        )
+
+        # forward fill missing windows (carry last known price)
+        windowed = windowed.sort_values([ts_col, 'productId'])
+        windowed['price'] = windowed.groupby('productId')['price'].ffill()
+        windowed = windowed.dropna(subset=['price'])
+
+        # group into chunks by window
+        chunks = []
+        for window_start, group in windowed.groupby(ts_col):
+            price_vector = group[['productId', 'price']].copy()
+
+            # fill missing products with last known price before this window
+            missing_products = set(unique_products) - set(price_vector['productId'])
+            if missing_products:
+                for pid in missing_products:
+                    last_price = df_indexed[
+                        (df_indexed['productId'] == pid) &
+                        (df_indexed.index < window_start)
+                    ]['price']
+
+                    if not last_price.empty:
+                        price_vector = pd.concat([
+                            price_vector,
+                            pd.DataFrame({'productId': [pid], 'price': [last_price.iloc[-1]]})
+                        ], ignore_index=True)
+
+            if not price_vector.empty:
+                chunks.append({
+                    'window_start': window_start,
+                    'window_end': window_start + pd.Timedelta(window_size),
+                    'price_vector': price_vector
+                })
+
+        return chunks
+
+
+class ComputeElasticityStep(BaseContextStep):
+    """
+    Compute price elasticity from demand and price chunks.
+    Input: (demand_chunks, price_chunks)
+    Output: elasticity_df [productId, elasticity, std_error, n_obs]
+    """
+
+    def transform(self, chunk_tuple: tuple):
+        demand_chunks, price_chunks = chunk_tuple
+
+        method = self.context.config.get('elasticity_method', 'point')
+        min_obs = self.context.config.get('min_observations', 2)
+
+        products = self.context.products
+        all_product_ids = products['id'].unique()
+
+        # align chunks by window_start
+        aligned = self._align_chunks(demand_chunks, price_chunks)
+
+        if not aligned:
+            return pd.DataFrame({
+                'productId': all_product_ids,
+                'elasticity': 0.0,
+                'std_error': 0.0,
+                'n_obs': 0
+            })
+
+        # build time series per product
+        product_series = self._build_timeseries(aligned)
+
+        # compute elasticity per product
+        elasticities = []
+        for pid, series in product_series.items():
+            if len(series) < min_obs:
+                elasticities.append({
+                    'productId': pid,
+                    'elasticity': 0.0,
+                    'std_error': 0.0,
+                    'n_obs': len(series)
+                })
+                continue
+
+            elast = self._compute_elasticity(series, method)
+            elasticities.append({
+                'productId': pid,
+                'elasticity': elast['value'],
+                'std_error': elast.get('std_error', 0.0),
+                'n_obs': len(series)
+            })
+
+        result_df = pd.DataFrame(elasticities)
+
+        # fill missing products with zero elasticity
+        observed_pids = set(result_df['productId'])
+        missing_pids = [p for p in all_product_ids if p not in observed_pids]
+
+        if missing_pids:
+            missing_df = pd.DataFrame({
+                'productId': missing_pids,
+                'elasticity': 0.0,
+                'std_error': 0.0,
+                'n_obs': 0
+            })
+            result_df = pd.concat([result_df, missing_df], ignore_index=True)
+
+        return result_df
+
+    def _align_chunks(self, demand_chunks: List[Dict], price_chunks: List[Dict]):
+        """Align demand and price chunks by window_start"""
+        price_lookup = {c['window_start']: c for c in price_chunks}
+        aligned = []
+
+        for dc in demand_chunks:
+            ws = dc['window_start']
+            if ws in price_lookup:
+                aligned.append({
+                    'window_start': ws,
+                    'window_end': dc['window_end'],
+                    'demand': dc['demand_vector'],
+                    'prices': price_lookup[ws]['price_vector']
+                })
+
+        return aligned
+
+    def _build_timeseries(self, aligned: List[Dict]):
+        """Build time series [timestamp, price, quantity] per product"""
+        series_by_product = {}
+
+        for chunk in aligned:
+            merged = chunk['demand'].merge(chunk['prices'], on='productId', how='inner')
+
+            for _, row in merged.iterrows():
+                pid = row['productId']
+                if pid not in series_by_product:
+                    series_by_product[pid] = []
+
+                series_by_product[pid].append({
+                    'timestamp': chunk['window_start'],
+                    'price': row['price'],
+                    'quantity': row['demand_score']
+                })
+
+        return series_by_product
+
+    def _compute_elasticity(self, series: List[Dict], method: str):
+        """Compute point or arc elasticity"""
+        prices = np.array([s['price'] for s in series])
+        quantities = np.array([s['quantity'] for s in series])
+
+        # filter out zero/negative values
+        valid = (prices > 0) & (quantities > 0)
+        if valid.sum() < 2:
+            return {'value': 0.0, 'std_error': 0.0}
+
+        prices = prices[valid]
+        quantities = quantities[valid]
+
+        if method == 'point':
+            return self._point_elasticity(prices, quantities)
+        elif method == 'arc':
+            return self._arc_elasticity(prices, quantities)
+        else:
+            raise ValueError(f"Unknown elasticity method: {method}")
+
+    def _point_elasticity(self, prices: np.ndarray, quantities: np.ndarray):
+        """Point elasticity via log-log regression: log(Q) = a + b*log(P), elasticity = b"""
+        if len(prices) < 2:
+            return {'value': 0.0, 'std_error': 0.0}
+
+        log_p = np.log(prices)
+        log_q = np.log(quantities)
+
+        if log_p.std() == 0:
+            return {'value': 0.0, 'std_error': 0.0}
+
+        cov = np.cov(log_p, log_q)[0, 1]
+        var = np.var(log_p)
+        b = cov / var
+
+        # std error estimate
+        if len(prices) > 2:
+            residuals = log_q - (log_q.mean() + b * (log_p - log_p.mean()))
+            mse = (residuals ** 2).sum() / (len(prices) - 2)
+            se_b = np.sqrt(mse / (len(prices) * var))
+        else:
+            se_b = 0.0
+
+        return {'value': b, 'std_error': se_b}
+
+    def _arc_elasticity(self, prices: np.ndarray, quantities: np.ndarray):
+        """Arc elasticity: average period-over-period elasticity"""
+        elasticities = []
+
+        for i in range(1, len(prices)):
+            p1, p2 = prices[i-1], prices[i]
+            q1, q2 = quantities[i-1], quantities[i]
+
+            p_avg = (p1 + p2) / 2
+            q_avg = (q1 + q2) / 2
+
+            if p_avg == 0 or q_avg == 0:
+                continue
+
+            delta_p = p2 - p1
+            delta_q = q2 - q1
+
+            if delta_p == 0:
+                continue
+
+            e = (delta_q / q_avg) / (delta_p / p_avg)
+            elasticities.append(e)
+
+        if not elasticities:
+            return {'value': 0.0, 'std_error': 0.0}
+
+        return {
+            'value': np.mean(elasticities),
+            'std_error': np.std(elasticities) / np.sqrt(len(elasticities))
+        }

experiments/procesing/steps/fetch.py

@@ -0,0 +1,46 @@
+import pandas as pd
+from procesing.steps.base import BaseContextStep
+
+class FetchInteractionsStep(BaseContextStep):
+    """Fetch raw interaction data from Kafka topic"""
+
+    def transform(self, X=None):
+        df = self.context.provider.fetch_kafka_topic('user-interactions')
+
+        if df.empty:
+            return df
+
+        # Explode metadata JSON column
+        if 'metadata' in df.columns:
+            df = df.join(
+                pd.json_normalize(df.pop('metadata'), sep='.').add_prefix('metadata_')
+            )
+
+        df = df.dropna(subset=['eventName'])
+
+        # Remap dateIndex if present
+        if 'metadata_dateIndex' in df.columns:
+            df['dateIndex'] = df['metadata_dateIndex'].astype('Int64')
+
+        return df
+
+
+class FetchPriceLogsStep(BaseContextStep):
+    """Fetch price log data from Kafka topic"""
+
+    def transform(self, X=None):
+        return self.context.provider.fetch_kafka_topic('price-logs')
+
+
+class FetchExperimentsStep(BaseContextStep):
+    """Fetch experiment metadata for given interaction data"""
+
+    def transform(self, interactions_df: pd.DataFrame):
+        if interactions_df.empty or 'experimentId' not in interactions_df.columns:
+            return pd.DataFrame()
+
+        exp_ids = interactions_df['experimentId'].dropna().unique().tolist()
+        if not exp_ids:
+            return pd.DataFrame()
+
+        return self.context.provider.fetch_experiments(exp_ids)

experiments/procesing/steps/join.py

@@ -0,0 +1,34 @@
+import pandas as pd
+from procesing.steps.base import BaseContextStep
+
+class JoinExperimentsStep(BaseContextStep):
+    """Join experiment metadata to interactions"""
+
+    def transform(self, data: tuple):
+        """
+        Args:
+            data: (interactions_df, experiments_df)
+        Returns:
+            merged interactions dataframe
+        """
+        interactions_df, experiments_df = data
+
+        if experiments_df.empty:
+            return interactions_df
+
+        # Flatten nested task field if present
+        if 'task' in experiments_df.columns and experiments_df['task'].notnull().any():
+            task_norm = pd.json_normalize(experiments_df['task'].dropna())
+            task_norm.index = experiments_df[experiments_df['task'].notnull()].index
+            experiments_df = experiments_df.drop('task', axis=1).join(task_norm, rsuffix='_task')
+
+        # Rename for clarity
+        experiments_df = experiments_df.rename(columns={
+            'id': 'experimentId',
+            'subject_name': 'exp_subject',
+            'xp_human_only': 'exp_human_only',
+            'xp_market_mode': 'exp_market_mode',
+            'xp_task_id': 'exp_task_id'
+        })
+
+        return interactions_df.merge(experiments_df, on='experimentId', how='left')

experiments/procesing/steps/pricing.py

@@ -0,0 +1,149 @@
+import numpy as np
+import pandas as pd
+from typing import Optional, List, Dict, Any
+from dataclasses import dataclass, field
+from procesing.steps.base import BaseContextStep
+from procesing.pricers import ElasticityBasedPricer
+
+@dataclass
+class StateSpace:
+    """
+    State representation for pricing functions.
+
+    Components:
+        Q_t: demand ∈ R^n (current demand signal per product)
+        P_t: prices ∈ R^n (current/base prices)
+        S_t: session_features (behavioral signals, interaction data)
+        H_t: history = {Q_{t-k}, P_{t-k}, S_{t-k}} for k in [1, history_length]
+
+    Additionally stores:
+        - product_ids: product identifiers (n,)
+        - elasticity: price elasticity per product (n,)
+        - metadata: arbitrary context (experiment_id, timestamp, etc.)
+    """
+    demand: np.ndarray  # Q_t ∈ R^n
+    prices: np.ndarray  # P_t ∈ R^n
+    session_features: pd.DataFrame = field(default_factory=pd.DataFrame)  # S_t
+
+    # augmented state components
+    product_ids: Optional[np.ndarray] = None
+    elasticity: Optional[np.ndarray] = None
+
+    # historical trajectory H_t = {(Q_{t-k}, P_{t-k}, S_{t-k})}
+    history: List[Dict[str, Any]] = field(default_factory=list)
+
+    # metadata for context
+    metadata: Dict[str, Any] = field(default_factory=dict)
+
+    def __post_init__(self):
+        """Validate dimensions."""
+        n = len(self.demand)
+        assert len(self.prices) == n, "demand and prices must have same dimension"
+        if self.elasticity is not None:
+            assert len(self.elasticity) == n, "elasticity must match dimension"
+        if self.product_ids is not None:
+            assert len(self.product_ids) == n, "product_ids must match dimension"
+
+    @property
+    def n_products(self) -> int:
+        """Number of products in state space."""
+        return len(self.demand)
+
+    def add_history(self, q: np.ndarray, p: np.ndarray, s: pd.DataFrame, max_length: int = 10):
+        """Append historical state to trajectory H_t."""
+        self.history.append({'demand': q, 'prices': p, 'session_features': s})
+        if len(self.history) > max_length:
+            self.history.pop(0)
+
+    def get_history_window(self, k: int = 5) -> List[Dict[str, Any]]:
+        """Retrieve last k historical states."""
+        return self.history[-k:] if len(self.history) >= k else self.history
+
+
+class BuildStateSpaceStep(BaseContextStep):
+    """
+    Build state space from elasticity, demand, and price data.
+
+    Input: elasticity_df [productId, elasticity, ...], optional demand_df
+    Output: StateSpace instance with Q_t, P_t, elasticity, product_ids
+    """
+
+    def transform(self, elasticity_df: pd.DataFrame, demand_df: Optional[pd.DataFrame] = None):
+        products = self.context.products
+
+        # extract base prices from product metadata
+        products_with_prices = products.copy()
+        if 'metadata' in products_with_prices.columns:
+            products_with_prices['base_price'] = products_with_prices['metadata'].apply(
+                lambda m: m.get('base_price', 0) if isinstance(m, dict) else 0
+            )
+        else:
+            products_with_prices['base_price'] = 0
+
+        # merge with elasticity
+        merged = products_with_prices[['id', 'base_price']].rename(
+            columns={'id': 'productId'}
+        ).merge(
+            elasticity_df[['productId', 'elasticity']],
+            on='productId',
+            how='left'
+        ).fillna({'elasticity': 0.0, 'base_price': 0.0})
+
+        # merge with demand if provided, else use default
+        if demand_df is not None and 'demand' in demand_df.columns:
+            merged = merged.merge(
+                demand_df[['productId', 'demand']],
+                on='productId',
+                how='left'
+            ).fillna({'demand': 0.0})
+            demand_vector = merged['demand'].values
+        else:
+            # default: uniform demand or use elasticity as proxy
+            demand_vector = np.ones(len(merged)) * 10.0
+
+        return StateSpace(
+            demand=demand_vector,
+            prices=merged['base_price'].values,
+            session_features=pd.DataFrame(),
+            product_ids=merged['productId'].values,
+            elasticity=merged['elasticity'].values,
+            metadata={'timestamp': pd.Timestamp.now().isoformat()}
+        )
+
+
+class FitPricingFunctionStep(BaseContextStep):
+    """
+    Fit pricing function using elasticity data.
+    Input: elasticity_df
+    Output: fitted pricing function instance
+    """
+
+    def transform(self, elasticity_df: pd.DataFrame):
+        pricing_class = self.context.config.get('pricing_function_class', ElasticityBasedPricer)
+        pricing_params = self.context.config.get('pricing_function_params', {})
+
+        pricer = pricing_class(**pricing_params)
+        pricer.fit(elasticity_df)
+
+        return pricer
+
+
+class PredictPricesStep(BaseContextStep):
+    """
+    Predict optimal prices using fitted pricing function.
+    Input: (pricer, state_space)
+    Output: prices_df [productId, predicted_price]
+    """
+
+    def transform(self, data: tuple):
+        pricer, state_space = data
+
+        products = self.context.products
+        product_ids = products['id'].values
+
+        predicted_prices = pricer.predict(state_space)
+
+        return pd.DataFrame({
+            'productId': product_ids,
+            'predicted_price': predicted_prices
+        })

experiments/procesing/steps/session.py

@@ -0,0 +1,114 @@
+"""
+Session feature extraction for S_t component of state space.
+Computes behavioral signals from interaction data already in pipeline.
+"""
+import pandas as pd
+import numpy as np
+from typing import Optional, Dict, Any
+from collections import Counter
+from procesing.steps.base import BaseContextStep
+
+
+class ExtractSessionFeaturesStep(BaseContextStep):
+    """
+    Extract session-level behavioral features from interaction logs.
+
+    Input: interactions_df (user-interactions from earlier pipeline step)
+    Output: session_features DataFrame [sessionId, feature_1, feature_2, ...]
+
+    Features computed:
+        - total_interactions: count of all events
+        - page_views, item_views, searches, cart_adds: event type counts
+        - hovers: hover event counts
+        - unique_products_viewed: distinct product IDs
+        - interaction_velocity: events per minute
+        - session_duration_sec: time span of session
+        - avg_time_between_events: mean inter-event time
+        - product_view_depth: max views for single product (attention signal)
+    """
+
+    def transform(self, interactions_df: pd.DataFrame) -> pd.DataFrame:
+        if interactions_df.empty:
+            return pd.DataFrame()
+
+        # ensure timestamp column
+        if 'ts' in interactions_df.columns:
+            interactions_df = interactions_df.copy()
+            interactions_df['ts'] = pd.to_datetime(interactions_df['ts'])
+
+        # group by session and compute features
+        session_features = []
+        for session_id, session_df in interactions_df.groupby('sessionId'):
+            features = self._extract_features_for_session(session_id, session_df)
+            session_features.append(features)
+
+        return pd.DataFrame(session_features)
+
+    def _extract_features_for_session(self, session_id: str, session_df: pd.DataFrame) -> Dict[str, Any]:
+        """Compute features for single session."""
+        features = {'sessionId': session_id}
+
+        # basic counts
+        features['total_interactions'] = len(session_df)
+
+        event_counts = session_df['eventName'].value_counts().to_dict()
+        features['page_views'] = event_counts.get('page_view', 0) + event_counts.get('view_item_page', 0)
+        features['item_views'] = event_counts.get('view_item_page', 0)
+        features['searches'] = event_counts.get('search', 0)
+        features['cart_adds'] = event_counts.get('add_item_to_cart', 0)
+
+        # hover events
+        hover_events = ['hover_over_title', 'hover_over_paragraph', 'hover_over_link', 'hover_over_button']
+        features['hovers'] = sum(event_counts.get(ev, 0) for ev in hover_events)
+
+        # product-level signals
+        product_ids = session_df['productId'].dropna()
+        features['unique_products_viewed'] = product_ids.nunique()
+
+        if len(product_ids) > 0:
+            product_view_counts = Counter(product_ids)
+            features['product_view_depth'] = max(product_view_counts.values())
+        else:
+            features['product_view_depth'] = 0
+
+        # temporal features
+        if 'ts' in session_df.columns:
+            timestamps = session_df['ts'].sort_values()
+            features['session_duration_sec'] = (timestamps.max() - timestamps.min()).total_seconds()
+
+            if features['session_duration_sec'] > 0:
+                features['interaction_velocity'] = (features['total_interactions'] / features['session_duration_sec']) * 60
+            else:
+                features['interaction_velocity'] = 0.0
+
+            # inter-event timing
+            if len(timestamps) > 1:
+                time_diffs = timestamps.diff().dropna().dt.total_seconds()
+                features['avg_time_between_events'] = time_diffs.mean()
+                features['std_time_between_events'] = time_diffs.std()
+            else:
+                features['avg_time_between_events'] = 0.0
+                features['std_time_between_events'] = 0.0
+        else:
+            features['session_duration_sec'] = 0.0
+            features['interaction_velocity'] = 0.0
+            features['avg_time_between_events'] = 0.0
+            features['std_time_between_events'] = 0.0
+
+        # cart/conversion signals
+        features['cart_to_view_ratio'] = features['cart_adds'] / features['item_views'] if features['item_views'] > 0 else 0.0
+
+        return features
+
+
+class FilterSessionInteractionsStep(BaseContextStep):
+    """
+    Filter interactions DataFrame to specific session.
+
+    Input: (interactions_df, session_id)
+    Output: interactions_df filtered to session_id
+    """
+
+    def transform(self, data: tuple) -> pd.DataFrame:
+        interactions_df, session_id = data
+        return interactions_df[interactions_df['sessionId'] == session_id].copy()

experiments/procesing/tests/conftest.py

@@ -0,0 +1,271 @@
+import pytest
+import pandas as pd
+from typing import List
+from procesing.providers.base import DataProvider
+from procesing.context import PipelineContext
+
+
+class MockProvider(DataProvider):
+    """Mock provider for testing, holds in-memory fixtures"""
+
+    def __init__(self, products_df=None, experiments_df=None, kafka_data=None):
+        self._products = products_df if products_df is not None else pd.DataFrame()
+        self._experiments = experiments_df if experiments_df is not None else pd.DataFrame()
+        self._kafka_data = kafka_data if kafka_data is not None else {}
+
+    def fetch_products(self, store_mode: str) -> pd.DataFrame:
+        return self._products.copy()
+
+    def fetch_experiments(self, experiment_ids: List[str]) -> pd.DataFrame:
+        if self._experiments.empty:
+            return pd.DataFrame()
+        return self._experiments[
+            self._experiments['id'].isin(experiment_ids)
+        ].copy()
+
+    def fetch_kafka_topic(self, topic: str) -> pd.DataFrame:
+        return self._kafka_data.get(topic, pd.DataFrame()).copy()
+
+
+@pytest.fixture
+def mock_products():
+    """Standard product catalog fixture with realistic IDs from test data"""
+    return pd.DataFrame({
+        'id': [
+            'd018efc1-25e9-4284-b276-80386e048b25',
+            '51266ddb-5b07-47b7-89ee-5b5cae94bb11',
+            '2cd7f756-fc65-4ba0-ab01-74521c1fff43'
+        ],
+        'name': ['Junior Suite', 'Superior Room', 'Deluxe Room'],
+        'base_price': [200.0, 150.0, 180.0]
+    })
+
+
+@pytest.fixture
+def mock_interactions_raw_kafka():
+    """Raw Kafka message structure for interactions, matches production format"""
+    return [
+        {
+            'partitionID': 0, 'offset': 203, 'timestamp': 1764102082676,
+            'value': {
+                'payload': {
+                    'sessionId': 'd423ce8a-77aa-4c9a-94d4-d1adddcc3472',
+                    'experimentId': '53aefd07-f66a-4d7f-ba8b-7ea1fc562d35',
+                    'eventName': 'learn_more_about_item',
+                    'page': '/hotel/products/d018efc1-25e9-4284-b276-80386e048b25',
+                    'productId': 'd018efc1-25e9-4284-b276-80386e048b25',
+                    'metadata': {'type': 'hotel', 'dateIndex': 1, 'roomType': 'Junior Suite'},
+                    'storeMode': 'hotel',
+                    'ts': '2025-11-25T20:21:22.674Z'
+                }
+            }
+        },
+        {
+            'partitionID': 0, 'offset': 204, 'timestamp': 1764102086982,
+            'value': {
+                'payload': {
+                    'sessionId': 'd423ce8a-77aa-4c9a-94d4-d1adddcc3472',
+                    'experimentId': '53aefd07-f66a-4d7f-ba8b-7ea1fc562d35',
+                    'eventName': 'page_view',
+                    'page': '/hotel/products',
+                    'productId': None,
+                    'metadata': {'referrer': ''},
+                    'storeMode': 'hotel',
+                    'ts': '2025-11-25T20:21:26.947Z'
+                }
+            }
+        },
+        {
+            'partitionID': 0, 'offset': 205, 'timestamp': 1764102091825,
+            'value': {
+                'payload': {
+                    'sessionId': 'd423ce8a-77aa-4c9a-94d4-d1adddcc3472',
+                    'experimentId': '53aefd07-f66a-4d7f-ba8b-7ea1fc562d35',
+                    'eventName': 'hover_over_title',
+                    'page': '/hotel/products',
+                    'productId': '51266ddb-5b07-47b7-89ee-5b5cae94bb11',
+                    'metadata': {'elementText': 'Superior Room', 'dateIndex': 1, 'dwellTime': 1200},
+                    'storeMode': 'hotel',
+                    'ts': '2025-11-25T20:21:31.823Z'
+                }
+            }
+        },
+        {
+            'partitionID': 0, 'offset': 206, 'timestamp': 1764102094193,
+            'value': {
+                'payload': {
+                    'sessionId': 'd423ce8a-77aa-4c9a-94d4-d1adddcc3472',
+                    'experimentId': 'bbbbcccc-dddd-eeee-ffff-000011112222',
+                    'eventName': 'hover_over_paragraph',
+                    'page': '/hotel/products',
+                    'productId': '51266ddb-5b07-47b7-89ee-5b5cae94bb11',
+                    'metadata': {'elementText': 'price', 'dateIndex': 1, 'dwellTime': 1307},
+                    'storeMode': 'hotel',
+                    'ts': '2025-11-25T20:21:34.191Z'
+                }
+            }
+        },
+        {
+            'partitionID': 0, 'offset': 207, 'timestamp': 1764102101970,
+            'value': {
+                'payload': {
+                    'sessionId': 'd423ce8a-77aa-4c9a-94d4-d1adddcc3472',
+                    'experimentId': 'bbbbcccc-dddd-eeee-ffff-000011112222',
+                    'eventName': 'hover_over_paragraph',
+                    'page': '/hotel/products',
+                    'productId': 'd018efc1-25e9-4284-b276-80386e048b25',
+                    'metadata': {'elementText': 'price', 'dateIndex': 1, 'dwellTime': 1201},
+                    'storeMode': 'hotel',
+                    'ts': '2025-11-25T20:21:41.967Z'
+                }
+            }
+        }
+    ]
+
+
+@pytest.fixture
+def mock_interactions(mock_interactions_raw_kafka):
+    """Processed interaction DataFrame (what provider.fetch_kafka_topic returns)"""
+    records = [msg['value']['payload'] for msg in mock_interactions_raw_kafka]
+    df = pd.DataFrame(records)
+    df['timestamp'] = pd.to_datetime(df['ts'])
+    return df
+
+
+@pytest.fixture
+def mock_price_logs_raw_kafka():
+    """Raw Kafka message structure for price logs, matches production format"""
+    return [
+        {
+            'partitionID': 0, 'offset': 32, 'timestamp': 1764104757969,
+            'value': {
+                'payload': {
+                    'productId': '2cd7f756-fc65-4ba0-ab01-74521c1fff43',
+                    'price': 162.47,
+                    'sessionId': 'd423ce8a-77aa-4c9a-94d4-d1adddcc3472',
+                    'experimentId': '53aefd07-f66a-4d7f-ba8b-7ea1fc562d35',
+                    'storeMode': 'shop',
+                    'ts': '2025-11-25T21:05:57.967Z'
+                }
+            }
+        },
+        {
+            'partitionID': 0, 'offset': 33, 'timestamp': 1764104757995,
+            'value': {
+                'payload': {
+                    'productId': '2ddabbfc-4127-48fc-86dc-ebc4c677efa2',
+                    'price': 743.49,
+                    'sessionId': 'd423ce8a-77aa-4c9a-94d4-d1adddcc3472',
+                    'experimentId': '53aefd07-f66a-4d7f-ba8b-7ea1fc562d35',
+                    'storeMode': 'shop',
+                    'ts': '2025-11-25T21:05:57.993Z'
+                }
+            }
+        },
+        {
+            'partitionID': 0, 'offset': 34, 'timestamp': 1764104758011,
+            'value': {
+                'payload': {
+                    'productId': '2cd7f756-fc65-4ba0-ab01-74521c1fff43',
+                    'price': 163.87,
+                    'sessionId': 'd423ce8a-77aa-4c9a-94d4-d1adddcc3472',
+                    'experimentId': '53aefd07-f66a-4d7f-ba8b-7ea1fc562d35',
+                    'storeMode': 'shop',
+                    'ts': '2025-11-25T21:05:58.009Z'
+                }
+            }
+        },
+        {
+            'partitionID': 0, 'offset': 35, 'timestamp': 1764104758050,
+            'value': {
+                'payload': {
+                    'productId': '2ddabbfc-4127-48fc-86dc-ebc4c677efa2',
+                    'price': 397.46,
+                    'sessionId': 'd423ce8a-77aa-4c9a-94d4-d1adddcc3472',
+                    'experimentId': '53aefd07-f66a-4d7f-ba8b-7ea1fc562d35',
+                    'storeMode': 'shop',
+                    'ts': '2025-11-25T21:05:58.049Z'
+                }
+            }
+        },
+        {
+            'partitionID': 0, 'offset': 36, 'timestamp': 1764104768865,
+            'value': {
+                'payload': {
+                    'productId': '2cd7f756-fc65-4ba0-ab01-74521c1fff43',
+                    'price': 401.66,
+                    'sessionId': 'd423ce8a-77aa-4c9a-94d4-d1adddcc3472',
+                    'experimentId': '53aefd07-f66a-4d7f-ba8b-7ea1fc562d35',
+                    'storeMode': 'shop',
+                    'ts': '2025-11-25T21:06:08.864Z'
+                }
+            }
+        }
+    ]
+
+
+@pytest.fixture
+def mock_price_logs(mock_price_logs_raw_kafka):
+    """Processed price logs DataFrame (what provider.fetch_kafka_topic returns)"""
+    # extract payloads and flatten
+    records = [msg['value']['payload'] for msg in mock_price_logs_raw_kafka]
+    df = pd.DataFrame(records)
+    df['timestamp'] = pd.to_datetime(df['ts'])
+    return df
+
+
+@pytest.fixture
+def mock_experiments():
+    """Standard experiment metadata fixture matching Supabase schema"""
+    return pd.DataFrame({
+        'id': ['53aefd07-f66a-4d7f-ba8b-7ea1fc562d35', 'bbbbcccc-dddd-eeee-ffff-000011112222'],
+        'created_at': pd.to_datetime(['2025-11-25T20:00:00Z', '2025-11-26T10:00:00Z']),
+        'subject_name': ['Session A', 'Session B'],
+        'xp_human_only': [True, False],
+        'xp_market_mode': ['hotel', 'shop'],
+        'xp_task_id': [None, None]
+    })
+
+
+@pytest.fixture
+def mock_provider(mock_products, mock_experiments, mock_interactions, mock_price_logs):
+    """Fully configured mock provider"""
+    return MockProvider(
+        products_df=mock_products,
+        experiments_df=mock_experiments,
+        kafka_data={
+            'user-interactions': mock_interactions,
+            'price-logs': mock_price_logs
+        }
+    )
+
+
+@pytest.fixture
+def pipeline_context(mock_provider):
+    """Standard pipeline context for testing"""
+    return PipelineContext(
+        provider=mock_provider,
+        store_mode='hotel',
+        window_size='30s',
+        n_price_buckets=3
+    )
+
+
+@pytest.fixture
+def empty_provider():
+    """Provider with no data, for edge case testing"""
+    return MockProvider(
+        products_df=pd.DataFrame(columns=['id', 'name', 'base_price']),
+        experiments_df=pd.DataFrame(columns=['id', 'created_at', 'subject_name', 'xp_human_only', 'xp_market_mode', 'xp_task_id']),
+        kafka_data={'user-interactions': pd.DataFrame(), 'price-logs': pd.DataFrame()}
+    )
+
+
+@pytest.fixture
+def empty_context(empty_provider):
+    """Context with empty provider"""
+    return PipelineContext(
+        provider=empty_provider,
+        store_mode='hotel',
+        window_size='30s'
+    )

experiments/procesing/tests/test_augement.py

@@ -0,0 +1,45 @@
+import pytest
+import random
+import pandas as pd
+from procesing.steps import (
+    CreatePriceBucketsStep,
+    AugmentEventNamesStep
+)
+
+def test_bucketing(pipeline_context):
+    step = CreatePriceBucketsStep(context=pipeline_context)
+
+    # Test with normal price data
+    df = pd.DataFrame({
+        'metadata_price': random.sample(range(10, 1000), 100)
+    })
+    result = step.transform(df)
+    assert 'price_bucket' in result.columns
+    # test if is categorical
+    assert isinstance(result['price_bucket'].dtype, pd.CategoricalDtype)
+    assert result['price_bucket'].nunique() == 3 # as per context config
+    # distribution check
+    counts = result['price_bucket'].value_counts()
+    assert all(counts > 0)
+    assert counts.max() - counts.min() <= 10  # roughly equal distribution for 100 samples
+    # Test with empty DataFrame
+    df = pd.DataFrame()
+    result = step.transform(df)
+    assert 'price_bucket' in result.columns
+    assert result.empty
+
+
+def test_augment_names(pipeline_context):
+    df = pd.DataFrame({
+        'eventName': ['click', 'view', 'purchase'],
+        'productId': ['prod_1', 'prod_2', None],
+        'price_bucket': ['PB_1', None, 'PB_3']
+    })
+    step = AugmentEventNamesStep(context=pipeline_context)
+    result = step.transform(df)
+    expected_event_names = [
+        'click_prod_1@PB_1',
+        'view',
+        'purchase'
+    ]
+    assert result['eventName'].tolist() == expected_event_names

experiments/procesing/tests/test_demand.py

@@ -0,0 +1,49 @@
+import pytest
+import random
+import pandas as pd
+from procesing.steps import (
+    ComputeDemandStep
+)
+
+def test_compute_demand(pipeline_context):
+    step = ComputeDemandStep(context=pipeline_context)
+
+    # Test with normal interaction data
+    df = pd.DataFrame({
+        'ts': pd.date_range(start='2023-01-01', periods=100, freq='h'),
+        'productId': random.choices([
+            'd018efc1-25e9-4284-b276-80386e048b25',
+            '51266ddb-5b07-47b7-89ee-5b5cae94bb11',
+            '2cd7f756-fc65-4ba0-ab01-74521c1fff43'
+        ], k=100),
+        'eventName': random.choices(['view', 'click', 'purchase'], k=100)
+    })
+    result = step.transform(df)
+    assert type(result) == pd.DataFrame
+    assert not result.empty
+    assert set(result['productId']) == set(pipeline_context.products['id'])
+    assert all(result['demand_score'] > 100/3 -10)
+
+
+def test_compute_demand_skewed(pipeline_context):
+    step = ComputeDemandStep(context=pipeline_context)
+
+    # Test with normal interaction data
+    df = pd.DataFrame({
+        'ts': pd.date_range(start='2023-01-01', periods=100, freq='h'),
+        'productId': random.choices([
+            'd018efc1-25e9-4284-b276-80386e048b25',
+            '51266ddb-5b07-47b7-89ee-5b5cae94bb11',
+            '2cd7f756-fc65-4ba0-ab01-74521c1fff43'
+        ], weights=[0.7, 0.2, 0.1], k=100),
+        'eventName': random.choices(['view', 'click', 'purchase'], k=100)
+    })
+    result = step.transform(df)
+    assert type(result) == pd.DataFrame
+    assert not result.empty
+    assert set(result['productId']) == set(pipeline_context.products['id'])
+    # test for skewness
+    scores = result.set_index('productId')['demand_score'].to_dict()
+    assert scores['d018efc1-25e9-4284-b276-80386e048b25'] > \
+           scores['51266ddb-5b07-47b7-89ee-5b5cae94bb11'] > \
+           scores['2cd7f756-fc65-4ba0-ab01-74521c1fff43']

experiments/procesing/tests/test_elasticity.py

@@ -0,0 +1,353 @@
+import pytest
+import pandas as pd
+import numpy as np
+from procesing.steps import (
+    AggregatePriceLogsStep,
+    ComputeElasticityStep
+)
+
+
+def test_aggregate_price_logs_basic(pipeline_context):
+    """Test basic price aggregation into time windows"""
+    step = AggregatePriceLogsStep(pipeline_context)
+
+    # Create price logs with known window structure
+    df = pd.DataFrame({
+        'ts': pd.date_range(start='2023-01-01 10:00:00', periods=100, freq='10s'),
+        'productId': np.tile([
+            'd018efc1-25e9-4284-b276-80386e048b25',
+            '51266ddb-5b07-47b7-89ee-5b5cae94bb11',
+            '2cd7f756-fc65-4ba0-ab01-74521c1fff43'
+        ], 34)[:100],
+        'price': np.random.uniform(100, 200, 100)
+    })
+
+    result = step.transform(df)
+    assert isinstance(result, list)
+    assert len(result) > 0
+    # each chunk should have window metadata and price vector
+    for chunk in result:
+        assert 'window_start' in chunk
+        assert 'window_end' in chunk
+        assert 'price_vector' in chunk
+        assert isinstance(chunk['price_vector'], pd.DataFrame)
+        assert 'productId' in chunk['price_vector'].columns
+        assert 'price' in chunk['price_vector'].columns
+
+
+def test_aggregate_price_logs_handles_gaps(pipeline_context):
+    """Test that price aggregation forward-fills missing windows"""
+    step = AggregatePriceLogsStep(pipeline_context)
+
+    # create sparse data with gaps
+    df = pd.DataFrame({
+        'ts': pd.to_datetime([
+            '2023-01-01 10:00:00',
+            '2023-01-01 10:00:05',
+            '2023-01-01 10:02:00',  # gap of ~2 mins
+            '2023-01-01 10:02:30'
+        ]),
+        'productId': [
+            'd018efc1-25e9-4284-b276-80386e048b25',
+            'd018efc1-25e9-4284-b276-80386e048b25',
+            '51266ddb-5b07-47b7-89ee-5b5cae94bb11',
+            '51266ddb-5b07-47b7-89ee-5b5cae94bb11'
+        ],
+        'price': [100, 102, 150, 153]
+    })
+
+    result = step.transform(df)
+    assert isinstance(result, list)
+    # should have multiple windows despite gaps
+    assert len(result) >= 2
+
+
+def test_compute_elasticity_with_known_relationship(pipeline_context):
+    """Test elasticity computation with known price-demand relationship"""
+    step = ComputeElasticityStep(pipeline_context)
+
+    # simulate elastic demand: when price ↑10%, demand ↓15% (elasticity ~ -1.5)
+    base_price = 100
+    base_demand = 50
+
+    demand_chunks = [
+        {
+            'window_start': pd.Timestamp('2023-01-01 10:00:00'),
+            'window_end': pd.Timestamp('2023-01-01 10:00:30'),
+            'demand_vector': pd.DataFrame({
+                'productId': ['d018efc1-25e9-4284-b276-80386e048b25'],
+                'demand_score': [base_demand]
+            })
+        },
+        {
+            'window_start': pd.Timestamp('2023-01-01 10:00:30'),
+            'window_end': pd.Timestamp('2023-01-01 10:01:00'),
+            'demand_vector': pd.DataFrame({
+                'productId': ['d018efc1-25e9-4284-b276-80386e048b25'],
+                'demand_score': [base_demand * 0.85]  # 15% decrease
+            })
+        },
+        {
+            'window_start': pd.Timestamp('2023-01-01 10:01:00'),
+            'window_end': pd.Timestamp('2023-01-01 10:01:30'),
+            'demand_vector': pd.DataFrame({
+                'productId': ['d018efc1-25e9-4284-b276-80386e048b25'],
+                'demand_score': [base_demand * 0.70]  # further decrease
+            })
+        }
+    ]
+
+    price_chunks = [
+        {
+            'window_start': pd.Timestamp('2023-01-01 10:00:00'),
+            'window_end': pd.Timestamp('2023-01-01 10:00:30'),
+            'price_vector': pd.DataFrame({
+                'productId': ['d018efc1-25e9-4284-b276-80386e048b25'],
+                'price': [base_price]
+            })
+        },
+        {
+            'window_start': pd.Timestamp('2023-01-01 10:00:30'),
+            'window_end': pd.Timestamp('2023-01-01 10:01:00'),
+            'price_vector': pd.DataFrame({
+                'productId': ['d018efc1-25e9-4284-b276-80386e048b25'],
+                'price': [base_price * 1.10]  # 10% increase
+            })
+        },
+        {
+            'window_start': pd.Timestamp('2023-01-01 10:01:00'),
+            'window_end': pd.Timestamp('2023-01-01 10:01:30'),
+            'price_vector': pd.DataFrame({
+                'productId': ['d018efc1-25e9-4284-b276-80386e048b25'],
+                'price': [base_price * 1.20]  # 20% increase
+            })
+        }
+    ]
+
+    result = step.transform((demand_chunks, price_chunks))
+    assert isinstance(result, pd.DataFrame)
+    assert not result.empty
+    assert 'productId' in result.columns
+    assert 'elasticity' in result.columns
+    assert 'n_obs' in result.columns
+
+    # check elasticity is negative (normal good)
+    product_elast = result[result['productId'] == 'd018efc1-25e9-4284-b276-80386e048b25']
+    assert len(product_elast) == 1
+    assert product_elast.iloc[0]['elasticity'] < 0
+    # should be roughly elastic (< -1)
+    assert product_elast.iloc[0]['n_obs'] == 3
+
+
+def test_compute_elasticity_inelastic_product(pipeline_context):
+    """Test with inelastic demand: price changes, demand barely moves"""
+    step = ComputeElasticityStep(pipeline_context)
+
+    base_price = 150
+    base_demand = 40
+
+    demand_chunks = [
+        {
+            'window_start': pd.Timestamp('2023-01-01 10:00:00'),
+            'window_end': pd.Timestamp('2023-01-01 10:00:30'),
+            'demand_vector': pd.DataFrame({
+                'productId': ['51266ddb-5b07-47b7-89ee-5b5cae94bb11'],
+                'demand_score': [base_demand]
+            })
+        },
+        {
+            'window_start': pd.Timestamp('2023-01-01 10:00:30'),
+            'window_end': pd.Timestamp('2023-01-01 10:01:00'),
+            'demand_vector': pd.DataFrame({
+                'productId': ['51266ddb-5b07-47b7-89ee-5b5cae94bb11'],
+                'demand_score': [base_demand * 0.98]  # tiny 2% decrease
+            })
+        }
+    ]
+
+    price_chunks = [
+        {
+            'window_start': pd.Timestamp('2023-01-01 10:00:00'),
+            'window_end': pd.Timestamp('2023-01-01 10:00:30'),
+            'price_vector': pd.DataFrame({
+                'productId': ['51266ddb-5b07-47b7-89ee-5b5cae94bb11'],
+                'price': [base_price]
+            })
+        },
+        {
+            'window_start': pd.Timestamp('2023-01-01 10:00:30'),
+            'window_end': pd.Timestamp('2023-01-01 10:01:00'),
+            'price_vector': pd.DataFrame({
+                'productId': ['51266ddb-5b07-47b7-89ee-5b5cae94bb11'],
+                'price': [base_price * 1.20]  # 20% increase
+            })
+        }
+    ]
+
+    result = step.transform((demand_chunks, price_chunks))
+    product_elast = result[result['productId'] == '51266ddb-5b07-47b7-89ee-5b5cae94bb11']
+    assert len(product_elast) == 1
+    # inelastic: elasticity between 0 and -1
+    assert -1 < product_elast.iloc[0]['elasticity'] < 0
+
+
+def test_compute_elasticity_multiple_products(pipeline_context):
+    """Test elasticity computation across multiple products simultaneously"""
+    step = ComputeElasticityStep(pipeline_context)
+
+    products = [
+        'd018efc1-25e9-4284-b276-80386e048b25',
+        '51266ddb-5b07-47b7-89ee-5b5cae94bb11',
+        '2cd7f756-fc65-4ba0-ab01-74521c1fff43'
+    ]
+
+    # create 5 time windows with all 3 products
+    demand_chunks = []
+    price_chunks = []
+
+    for i in range(5):
+        ts = pd.Timestamp('2023-01-01 10:00:00') + pd.Timedelta(f'{i*30}s')
+
+        demand_chunks.append({
+            'window_start': ts,
+            'window_end': ts + pd.Timedelta('30s'),
+            'demand_vector': pd.DataFrame({
+                'productId': products,
+                'demand_score': [
+                    50 * (0.9 ** i),  # elastic: decreases as price rises
+                    40 * (0.98 ** i), # inelastic: barely changes
+                    30 * (0.85 ** i)  # very elastic
+                ]
+            })
+        })
+
+        price_chunks.append({
+            'window_start': ts,
+            'window_end': ts + pd.Timedelta('30s'),
+            'price_vector': pd.DataFrame({
+                'productId': products,
+                'price': [
+                    100 * (1.05 ** i),
+                    150 * (1.10 ** i),
+                    120 * (1.08 ** i)
+                ]
+            })
+        })
+
+    result = step.transform((demand_chunks, price_chunks))
+    assert isinstance(result, pd.DataFrame)
+    assert len(result) == 3  # all products should have elasticity
+    assert set(result['productId']) == set(products)
+    assert all(result['n_obs'] == 5)
+    assert all(result['elasticity'] < 0)  # all normal goods
+
+
+def test_compute_elasticity_insufficient_data(pipeline_context):
+    """Test behavior with insufficient observations"""
+    step = ComputeElasticityStep(pipeline_context)
+
+    # only 1 observation
+    demand_chunks = [{
+        'window_start': pd.Timestamp('2023-01-01 10:00:00'),
+        'window_end': pd.Timestamp('2023-01-01 10:00:30'),
+        'demand_vector': pd.DataFrame({
+            'productId': ['d018efc1-25e9-4284-b276-80386e048b25'],
+            'demand_score': [50]
+        })
+    }]
+
+    price_chunks = [{
+        'window_start': pd.Timestamp('2023-01-01 10:00:00'),
+        'window_end': pd.Timestamp('2023-01-01 10:00:30'),
+        'price_vector': pd.DataFrame({
+            'productId': ['d018efc1-25e9-4284-b276-80386e048b25'],
+            'price': [100]
+        })
+    }]
+
+    result = step.transform((demand_chunks, price_chunks))
+    # should still return result but with low n_obs
+    product_elast = result[result['productId'] == 'd018efc1-25e9-4284-b276-80386e048b25']
+    assert len(product_elast) == 1
+    assert product_elast.iloc[0]['n_obs'] == 1
+    assert product_elast.iloc[0]['elasticity'] == 0.0  # not enough data
+
+
+def test_compute_elasticity_misaligned_chunks(pipeline_context):
+    """Test with non-overlapping demand and price windows"""
+    step = ComputeElasticityStep(pipeline_context)
+
+    demand_chunks = [{
+        'window_start': pd.Timestamp('2023-01-01 10:00:00'),
+        'window_end': pd.Timestamp('2023-01-01 10:00:30'),
+        'demand_vector': pd.DataFrame({
+            'productId': ['d018efc1-25e9-4284-b276-80386e048b25'],
+            'demand_score': [50]
+        })
+    }]
+
+    price_chunks = [{
+        'window_start': pd.Timestamp('2023-01-01 11:00:00'),  # different time
+        'window_end': pd.Timestamp('2023-01-01 11:00:30'),
+        'price_vector': pd.DataFrame({
+            'productId': ['d018efc1-25e9-4284-b276-80386e048b25'],
+            'price': [100]
+        })
+    }]
+
+    result = step.transform((demand_chunks, price_chunks))
+    # should handle gracefully with no aligned data
+    assert isinstance(result, pd.DataFrame)
+    assert all(result['n_obs'] == 0)
+
+
+def test_elasticity_arc_method(pipeline_context):
+    """Test arc elasticity computation method"""
+    # configure context for arc method
+    pipeline_context.config['elasticity_method'] = 'arc'
+    step = ComputeElasticityStep(pipeline_context)
+
+    demand_chunks = [
+        {
+            'window_start': pd.Timestamp('2023-01-01 10:00:00'),
+            'window_end': pd.Timestamp('2023-01-01 10:00:30'),
+            'demand_vector': pd.DataFrame({
+                'productId': ['d018efc1-25e9-4284-b276-80386e048b25'],
+                'demand_score': [100]
+            })
+        },
+        {
+            'window_start': pd.Timestamp('2023-01-01 10:00:30'),
+            'window_end': pd.Timestamp('2023-01-01 10:01:00'),
+            'demand_vector': pd.DataFrame({
+                'productId': ['d018efc1-25e9-4284-b276-80386e048b25'],
+                'demand_score': [80]
+            })
+        }
+    ]
+
+    price_chunks = [
+        {
+            'window_start': pd.Timestamp('2023-01-01 10:00:00'),
+            'window_end': pd.Timestamp('2023-01-01 10:00:30'),
+            'price_vector': pd.DataFrame({
+                'productId': ['d018efc1-25e9-4284-b276-80386e048b25'],
+                'price': [100]
+            })
+        },
+        {
+            'window_start': pd.Timestamp('2023-01-01 10:00:30'),
+            'window_end': pd.Timestamp('2023-01-01 10:01:00'),
+            'price_vector': pd.DataFrame({
+                'productId': ['d018efc1-25e9-4284-b276-80386e048b25'],
+                'price': [110]
+            })
+        }
+    ]
+
+    result = step.transform((demand_chunks, price_chunks))
+    product_elast = result[result['productId'] == 'd018efc1-25e9-4284-b276-80386e048b25']
+    assert len(product_elast) == 1
+    assert product_elast.iloc[0]['elasticity'] < 0
+    # reset config
+    pipeline_context.config['elasticity_method'] = 'point'

experiments/procesing/tests/test_fetch.py

@@ -0,0 +1,51 @@
+import pytest
+import pandas as pd
+from procesing.steps import (
+    FetchInteractionsStep,
+    FetchPriceLogsStep,
+    FetchExperimentsStep,
+)
+
+
+def test_fetch_interactions_data(pipeline_context):
+    step = FetchInteractionsStep(pipeline_context)
+    data = step.transform(None)
+    assert data is not None
+    assert isinstance(data, pd.DataFrame)
+    expected_cols = [
+        "eventName",
+        "dateIndex",
+        "experimentId",
+        "storeMode",
+        "metadata_elementText"
+    ]
+    for expected in expected_cols:
+        assert expected in data.columns
+
+def test_fetch_price_logs(pipeline_context):
+    step = FetchPriceLogsStep(pipeline_context)
+    data = step.transform(None)
+    assert data is not None
+    assert isinstance(data, pd.DataFrame)
+    expected_cols = [
+        "price",
+        "productId"
+    ]
+    for expected in expected_cols:
+        assert expected in data.columns
+    prices = data['price'].to_list()
+    assert min(prices) >= 0
+    assert max(prices) <= 9999
+
+
+def test_experiments_fetching(pipeline_context):
+    interactions = FetchInteractionsStep(pipeline_context).transform(None)
+    assert interactions is not None
+    experiments = FetchExperimentsStep(pipeline_context)
+    experiment_data = experiments.transform(interactions)
+    assert experiment_data is not None
+    assert isinstance(experiment_data, pd.DataFrame)
+    assert not experiment_data.empty
+    assert 'id' in experiment_data.columns
+    assert len(experiment_data) == 2
+    assert '53aefd07-f66a-4d7f-ba8b-7ea1fc562d35' in experiment_data['id'].values

experiments/procesing/tests/test_pricing.py

@@ -0,0 +1,87 @@
+import pytest
+import pandas as pd
+
+from procesing.pricers import (
+    StaticPricer,
+    RandomPricer,
+    ElasticityBasedPricer
+)
+
+
+def test_static_pricer_fit_and_predict():
+    # Sample historical data
+    historical_data = pd.DataFrame({
+        'product_id': [1, 2, 3],
+        'base_price': [100.0, 150.0, 200.0]
+    })
+
+    # Initialize and fit StaticPricer
+    pricer = StaticPricer()
+    pricer.fit(historical_data)
+
+    # Predict prices
+    predicted_prices = pricer.predict(None)
+
+    # Assert that predicted prices match base prices
+    expected_prices = historical_data['base_price'].values
+    assert all(predicted_prices == expected_prices), "Predicted prices do not match base prices"
+
+
+def test_random_pricer_fit_and_predict():
+    # Sample historical data
+    historical_data = pd.DataFrame({
+        'product_id': [1, 2, 3],
+        'base_price': [100.0, 150.0, 200.0]
+    })
+
+    # Initialize and fit RandomPricer
+    pricer = RandomPricer(price_min=50.0, price_max=250.0, seed=42)
+    pricer.fit(historical_data)
+
+    # Predict prices
+    predicted_prices = pricer.predict(None)
+
+    # Assert that predicted prices are within bounds
+    assert predicted_prices.min() >= 50.0, "Predicted prices are below minimum bound"
+    assert predicted_prices.max() <= 250.0, "Predicted prices are above maximum bound"
+    # distribution check (not so strict)
+    assert len(set(predicted_prices)) > 1, "Predicted prices are not varied enough"
+    assert len(predicted_prices) == len(historical_data), "Number of predicted prices does not match number of products"
+
+def test_elasticity_based_pricer_fit_and_predict():
+    # Sample historical data
+    historical_data = pd.DataFrame({
+        'productId': [1, 2, 3],
+        'elasticity': [-1.5, -0.5, -2.0],
+        'base_price': [100.0, 150.0, 200.0],
+        'mean_demand': [10, 20, 15]
+    })
+
+    # Initialize and fit ElasticityBasedPricer
+    pricer = ElasticityBasedPricer(alpha=0.1, price_floor=50.0, price_ceil=300.0)
+    pricer.fit(historical_data)
+
+    # Create a mock state space with demand deviations
+    class MockStateSpace:
+        def __init__(self, demand):
+            self.demand = demand
+
+    # Simulate demand higher than mean for all products
+    state_space = MockStateSpace(demand=[15, 25, 20])
+
+    # Predict prices
+    predicted_prices = pricer.predict(state_space)
+
+    # Assert that predicted prices are within bounds
+    assert predicted_prices.min() >= 50.0, "Predicted prices are below minimum bound"
+    assert predicted_prices.max() <= 300.0, "Predicted prices are above maximum bound"
+    assert len(predicted_prices) == len(historical_data), "Number of predicted prices does not match number of products"
+
+    # now we gotta check semantic validity
+    # since demand is higher than mean, prices should generally increase
+    for i, row in historical_data.iterrows():
+        base_price = row['base_price']
+        elasticity = row['elasticity']
+        expected_increase = base_price * (1 + 0.1 * abs(elasticity) * ((state_space.demand[i] - row['mean_demand']) / row['mean_demand']))
+        assert predicted_prices[i] >= base_price, f"Predicted price for product {row['productId']} did not increase as expected"
+        assert abs(predicted_prices[i] - expected_increase) < 1e-5, f"Predicted price for product {row['productId']} does not match expected calculation within 1e-5 tolerance"

experiments/pytest.ini

@@ -0,0 +1,8 @@
+[pytest]
+pythonpath = .
+testpaths = procesing/tests agents
+python_files = test*.py
+python_classes = Test*
+python_functions = test_*
+asyncio_mode = auto
+asyncio_default_fixture_loop_scope = function

experiments/seed_products.py

@@ -0,0 +1,125 @@
+import random
+import json
+import os
+import logging
+from dotenv import load_dotenv
+from supabase import create_client, Client
+from tqdm import tqdm
+
+load_dotenv()
+
+logging.basicConfig(level=logging.INFO, format='%(levelname)s: %(message)s')
+log = logging.getLogger(__name__)
+
+SUPABASE_URL = os.getenv("NEXT_PUBLIC_SUPABASE_URL")
+SUPABASE_SERVICE_KEY = os.getenv("SUPABASE_SERVICE_ROLE_KEY")
+
+if not SUPABASE_SERVICE_KEY:
+    log.error("SUPABASE_SERVICE_ROLE_KEY not found in environment")
+    raise ValueError("Missing SUPABASE_SERVICE_ROLE_KEY - required for admin operations")
+
+supabase: Client = create_client(SUPABASE_URL, SUPABASE_SERVICE_KEY)
+
+DAYS = 14
+
+# hotel room configurations
+ROOMS = {
+    "Presidential Suite": {'amenities': ['ocean_view', 'balcony', 'jacuzzi', 'butler_service', 'premium_minibar'], 'total': 1, 'image_url': "", "base_price": 450, 'name': 'Presidential Suite', 'refundable': True, 'max_occupancy': 4},
+    "Executive Suite": {'amenities': ['city_view', 'balcony', 'workspace', 'lounge_access'], 'total': 2, 'image_url': "", "base_price": 280, 'name': 'Executive Suite', 'refundable': True, 'max_occupancy': 3},
+    "Junior Suite": {'amenities': ['garden_view', 'mini_fridge', 'coffee_maker'], 'total': 5, 'image_url': "", "base_price": 180, 'name': 'Junior Suite', 'refundable': True, 'max_occupancy': 2},
+    "Deluxe Room": {'amenities': ['city_view', 'work_desk', 'coffee_maker'], 'total': 8, 'image_url': "", "base_price": 140, 'name': 'Deluxe Room', 'refundable': False, 'max_occupancy': 2},
+    "Superior Room": {'amenities': ['wifi', 'tv', 'safe'], 'total': 12, 'image_url': "", "base_price": 110, 'name': 'Superior Room', 'refundable': False, 'max_occupancy': 2},
+    "Standard Room": {'amenities': ['wifi', 'tv'], 'total': 20, 'image_url': "", "base_price": 85, 'name': 'Standard Room', 'refundable': False, 'max_occupancy': 2},
+}
+
+# flight configurations
+FLIGHTS = {
+    "JFK-LAX-Economy": {'departure': {'time': '08:00', 'airport': 'JFK'}, 'arrival': {'time': '11:30', 'airport': 'LAX'}, 'duration': '5h 30m', 'stops': 0, 'cabin_class': 'economy', 'fare_rule': 'standard', 'refundable': False, 'total': 180, 'base_price': 250},
+    "JFK-LAX-Business": {'departure': {'time': '08:00', 'airport': 'JFK'}, 'arrival': {'time': '11:30', 'airport': 'LAX'}, 'duration': '5h 30m', 'stops': 0, 'cabin_class': 'business', 'fare_rule': 'flexible', 'refundable': True, 'total': 30, 'base_price': 850},
+    "ORD-MIA-Economy": {'departure': {'time': '14:15', 'airport': 'ORD'}, 'arrival': {'time': '18:45', 'airport': 'MIA'}, 'duration': '3h 30m', 'stops': 0, 'cabin_class': 'economy', 'fare_rule': 'basic', 'refundable': False, 'total': 200, 'base_price': 180},
+    "SFO-SEA-Premium": {'departure': {'time': '06:30', 'airport': 'SFO'}, 'arrival': {'time': '08:45', 'airport': 'SEA'}, 'duration': '2h 15m', 'stops': 0, 'cabin_class': 'premium', 'fare_rule': 'standard', 'refundable': False, 'total': 60, 'base_price': 420},
+    "ATL-DFW-First": {'departure': {'time': '16:00', 'airport': 'ATL'}, 'arrival': {'time': '17:30', 'airport': 'DFW'}, 'duration': '2h 30m', 'stops': 0, 'cabin_class': 'first', 'fare_rule': 'flexible', 'refundable': True, 'total': 12, 'base_price': 1600},
+    "LAX-SFO-Economy": {'departure': {'time': '10:00', 'airport': 'LAX'}, 'arrival': {'time': '11:30', 'airport': 'SFO'}, 'duration': '1h 30m', 'stops': 0, 'cabin_class': 'economy', 'fare_rule': 'standard', 'refundable': False, 'total': 150, 'base_price': 120},
+    "MIA-ATL-Premium": {'departure': {'time': '19:00', 'airport': 'MIA'}, 'arrival': {'time': '20:45', 'airport': 'ATL'}, 'duration': '1h 45m', 'stops': 0, 'cabin_class': 'premium', 'fare_rule': 'standard', 'refundable': True, 'total': 50, 'base_price': 380},
+    "DFW-ORD-Economy": {'departure': {'time': '07:30', 'airport': 'DFW'}, 'arrival': {'time': '10:15', 'airport': 'ORD'}, 'duration': '2h 45m', 'stops': 0, 'cabin_class': 'economy', 'fare_rule': 'basic', 'refundable': False, 'total': 190, 'base_price': 160},
+    "SEA-LAX-Business": {'departure': {'time': '13:00', 'airport': 'SEA'}, 'arrival': {'time': '15:30', 'airport': 'LAX'}, 'duration': '2h 30m', 'stops': 0, 'cabin_class': 'business', 'fare_rule': 'flexible', 'refundable': True, 'total': 40, 'base_price': 720},
+    "LAX-JFK-First": {'departure': {'time': '18:00', 'airport': 'LAX'}, 'arrival': {'time': '02:15', 'airport': 'JFK'}, 'duration': '5h 15m', 'stops': 0, 'cabin_class': 'first', 'fare_rule': 'flexible', 'refundable': True, 'total': 16, 'base_price': 1850},
+}
+
+def gen_hotel_products():
+    """generate hotel room products for next DAYS days"""
+    data = []
+    for day in range(DAYS):
+        for room_type, rdata in ROOMS.items():
+            data.append({
+                'room_type': room_type,
+                'date_index': day + 1,
+                'metadata': rdata,
+                'availability': random.randint(0, rdata['total'])
+            })
+    return data
+
+def gen_airline_products():
+    """generate flight products for next DAYS days"""
+    data = []
+    for day in range(DAYS):
+        for flight_type, fdata in FLIGHTS.items():
+            data.append({
+                'flight_type': flight_type,
+                'date_index': day + 1,
+                'metadata': fdata,
+                'availability': random.randint(0, fdata['total'])
+            })
+    return data
+
+def clear_table(table_name: str):
+    """clear all records from a table"""
+    try:
+        resp = supabase.table(table_name).select('id').execute()
+        if resp.data:
+            ids = [row['id'] for row in resp.data]
+            chunk_size = 100
+            for i in tqdm(range(0, len(ids), chunk_size), desc=f"Clearing {table_name}", unit="chunk"):
+                chunk = ids[i:i+chunk_size]
+                supabase.table(table_name).delete().in_('id', chunk).execute()
+            log.info(f"Deleted {len(ids)} records from {table_name}")
+        else:
+            log.info(f"{table_name} already empty")
+    except Exception as e:
+        log.error(f"Failed to clear {table_name}: {e}")
+        raise
+
+def seed_table(table_name: str, data: list[dict]):
+    """insert records into a table"""
+    try:
+        chunk_size = 100
+        total = len(data)
+        for i in tqdm(range(0, total, chunk_size), desc=f"Seeding {table_name}", unit="chunk"):
+            chunk = data[i:i+chunk_size]
+            supabase.table(table_name).insert(chunk).execute()
+        log.info(f"Inserted {total} records into {table_name}")
+    except Exception as e:
+        log.error(f"Failed to seed {table_name}: {e}")
+        raise
+
+def main():
+
+    log.info("Generating hotel products...")
+    hotel_products = gen_hotel_products()
+    log.info(f"Generated {len(hotel_products)} hotel products")
+
+    log.info("Generating airline products...")
+    airline_products = gen_airline_products()
+    log.info(f"Generated {len(airline_products)} airline products\n")
+
+    log.info("Clearing existing products...")
+    clear_table('hotel_products')
+    clear_table('airline_products')
+
+    log.info("Seeding products...")
+    seed_table('hotel_products', hotel_products)
+    seed_table('airline_products', airline_products)
+
+
+if __name__ == "__main__":
+    main()

lib/model_registry.py

@@ -0,0 +1,139 @@
+import redis
+import pickle
+import json
+import pandas as pd
+from typing import Optional, Dict, Any
+import os
+import logging
+log = logging.getLogger(__name__)
+
+class ModelRegistry:
+    """
+    Lightweight model registry using Redis for storing pricing models and elasticity data.
+    Models are serialized using pickle, metadata stored as JSON.
+    """
+
+    def __init__(self, redis_host: str = None, redis_port: int = None):
+        host = redis_host or os.getenv('REDIS_HOST', 'localhost')
+        port = redis_port or int(os.getenv('REDIS_PORT', '6378'))
+
+        self.redis_client = redis.Redis(
+            host=host,
+            port=port,
+            db=0,
+            decode_responses=False
+        )
+        self.metadata_prefix = "model:meta:"
+        self.data_prefix = "model:data:"
+        self.elasticity_prefix = "elasticity:"
+
+    def publish_elasticity(self,
+                          elasticity_df: pd.DataFrame,
+                          model_name: str = 'latest',
+                          metadata: Optional[Dict[str, Any]] = None):
+        """
+        Store elasticity estimates in registry.
+
+        Args:
+            elasticity_df: df with [productId, elasticity, std_error, n_obs]
+            model_name: identifier for this elasticity snapshot
+            metadata: additional info (timestamp, window_size, etc)
+        """
+        key = f"{self.elasticity_prefix}{model_name}"
+
+        # serialize dataframe as JSON
+        data_json = elasticity_df.to_json(orient='records')
+
+        # store data
+        self.redis_client.set(key, data_json)
+
+        # store metadata
+        meta = metadata or {}
+        meta.update({
+            'n_products': len(elasticity_df),
+            'mean_elasticity': float(elasticity_df['elasticity'].mean()),
+            'model_type': 'elasticity_snapshot'
+        })
+
+        meta_key = f"{self.metadata_prefix}{model_name}"
+        self.redis_client.set(meta_key, json.dumps(meta))
+
+        log.info(f"Published elasticity model '{model_name}' with {len(elasticity_df)} products")
+
+    def get_elasticity(self, model_name: str = 'latest') -> Optional[pd.DataFrame]:
+        """Retrieve elasticity estimates from registry."""
+        key = f"{self.elasticity_prefix}{model_name}"
+        data_json = self.redis_client.get(key)
+
+        if data_json is None:
+            return None
+
+        # decode bytes to string if needed
+        if isinstance(data_json, bytes):
+            data_json = data_json.decode('utf-8')
+
+        return pd.read_json(data_json, orient='records')
+
+    def publish_pricing_model(self,
+                              pricing_function,
+                              model_name: str = 'latest',
+                              metadata: Optional[Dict[str, Any]] = None):
+        """
+        Store a fitted pricing function object.
+
+        Args:
+            pricing_function: fitted PricingFunction instance
+            model_name: identifier
+            metadata: additional info
+        """
+        key = f"{self.data_prefix}{model_name}"
+
+        # serialize object
+        model_bytes = pickle.dumps(pricing_function)
+        self.redis_client.set(key, model_bytes)
+
+        # store metadata
+        meta = metadata or {}
+        meta.update({
+            'model_class': pricing_function.__class__.__name__,
+            'model_type': 'pricing_function'
+        })
+
+        meta_key = f"{self.metadata_prefix}{model_name}"
+        self.redis_client.set(meta_key, json.dumps(meta))
+
+        log.info(f"Published pricing model '{model_name}' ({meta['model_class']})")
+
+    def get_pricing_model(self, model_name: str = 'latest'):
+        """Retrieve a pricing function from registry."""
+        key = f"{self.data_prefix}{model_name}"
+        model_bytes = self.redis_client.get(key)
+
+        if model_bytes is None:
+            return None
+
+        return pickle.loads(model_bytes)
+
+    def list_models(self) -> Dict[str, Any]:
+        """List all registered models with metadata."""
+        models = {}
+
+        for key in self.redis_client.scan_iter(f"{self.metadata_prefix}*"):
+            key_str = key.decode('utf-8') if isinstance(key, bytes) else key
+            model_name = key_str.replace(self.metadata_prefix, '')
+            meta_json = self.redis_client.get(key)
+
+            if meta_json:
+                if isinstance(meta_json, bytes):
+                    meta_json = meta_json.decode('utf-8')
+                models[model_name] = json.loads(meta_json)
+
+        return models
+
+    def health_check(self) -> bool:
+        """Check if Redis connection is alive."""
+        try:
+            self.redis_client.ping()
+            return True
+        except:
+            return False

paper/concat_code.sh

@@ -16,11 +16,15 @@ mkdir -p "$(dirname "$OUTPUT_FILE")"
 add_file() {
     local filepath="$1"
     local relpath="${filepath#$PROJECT_ROOT/}"
+    local escaped_path="${relpath//_/\\_}"
 
     # Add section header and code listing (no language-specific highlighting)
-    echo "\\subsection{${relpath}}" >> "$OUTPUT_FILE"
-    echo "\\begin{lstlisting}[caption={${relpath}}]" >> "$OUTPUT_FILE"
-    cat "$filepath" >> "$OUTPUT_FILE"
+    echo "\\subsection{${escaped_path}}" >> "$OUTPUT_FILE"
+    echo "\\begin{lstlisting}[caption={${escaped_path}}]" >> "$OUTPUT_FILE"
+    # Convert to ASCII: transliterate what's possible, drop the rest
+    # LC_ALL=C forces ASCII locale for consistent behavior across environments
+    LC_ALL=C iconv -f UTF-8 -t ASCII//TRANSLIT//IGNORE "$filepath" 2>/dev/null >> "$OUTPUT_FILE" || \
+        LC_ALL=C tr -cd '\11\12\15\40-\176' < "$filepath" >> "$OUTPUT_FILE"
     echo "" >> "$OUTPUT_FILE"
     echo "\\end{lstlisting}" >> "$OUTPUT_FILE"
     echo "" >> "$OUTPUT_FILE"

paper/src/auto/main.el

@@ -10,11 +10,15 @@
    (TeX-run-style-hooks
     "latex2e"
     "preamble"
+    "chapters/01-intro"
+    "chapters/02-literature-review"
+    "chapters/03-methodology"
+    "chapters/04-results"
+    "chapters/05-discussion"
+    "chapters/06-conclusion"
+    "../build/concatenated_code"
     "acmart"
     "acmart10")
    (TeX-add-symbols
-    '("footnotetextcopyrightpermission" 1))
-   (LaTeX-add-labels
-    "research"))
+    '("footnotetextcopyrightpermission" 1)))
  :latex)
-

paper/src/bib/references.bib

@@ -1,106 +0,0 @@
-@techReport{,
-   abstract = {We consider a single product revenue management problem where, given an initial inventory, the objective is to dynamically adjust prices over a finite sales horizon to maximize expected revenues. Realized demand is observed over time, but the underlying functional relationship between price and mean demand rate that governs these observations (otherwise known as the demand function or demand curve), is not known. We consider two instances of this problem: i.) a setting where the demand function is assumed to belong to a known parametric family with unknown parameter values; and ii.) a setting where the demand function is assumed to belong to a broad class of functions that need not admit any parametric representation. In each case we develop policies that learn the demand function "on the fly," and optimize prices based on that. The performance of these algorithms is measured in terms of the regret: the revenue loss relative to the maximal revenues that can be extracted when the demand function is known prior to the start of the selling season. We derive lower bounds on the regret that hold for any admissible pricing policy, and then show that our proposed algorithms achieve a regret that is "close" to this lower bound. The magnitude of the regret can be interpreted as the economic value of prior knowledge on the demand function; manifested as the revenue loss due to model uncertainty.},
-   author = {Omar Besbes and Assaf Zeevi},
-   journal = {Operations Research},
-   keywords = {Revenue management,asymptotic analysis,estimation,exploration-exploitation,learning,pricing,value of information},
-   title = {Dynamic Pricing Without Knowing the Demand Function: Risk Bounds and Near-Optimal Algorithms *}
-}
-
-@misc{Ghaffary,
-   author = {Shirin Ghaffary and Matt Day},
-   note = {Updated 2025-11-05},
-   title = {Amazon Sues to Stop Perplexity From Using AI Tool to Buy Stuff},
-   url = {https://www.bloomberg.com/news/articles/2025-11-04/amazon-demands-perplexity-stop-ai-agent-from-making-purchases}
-}
-@phdthesis{,
-   abstract = {Algorithmic pricing is an emerging business practice that uses computational algorithms to determine
-the prices of products and services based on a number of dynamic factors. The aim of this thesis is to
-draw attention to the existence of these business practices, and the ethical and social implications that
-derive from them, and then focus on what could be effective solutions to increase the well-being of
-the community.
-In Chapter 2 of the thesis, a general introduction to the topic will be made, starting from its history
-and its evolution over the years; Chapter 3 will examine the different types of pricing algorithms.
-Subsequently, in Chapter 4 we will analyze the sectors in which they are most applicable, and the
-relative advantages and disadvantages they bring with them, with a critical analysis of the trade-offs
-generated. The effect of algorithmic pricing on competition will be studied, considering how the
-ability of algorithms to adapt quickly to market conditions can foster anti-competitive practices, such
-as price discrimination. Later, in Chapter 5, we will look at the issue of price transparency and how
-the opacity of algorithms can make it difficult for consumers to understand the pricing process and
-assess whether they are receiving fair treatment.
-To address these ethical issues, several possible solutions will be brought to light, described in
-Chapter 6, which will focus on the role of the government, as a regulatory, of the end consumer, who
-must be encouraged to educate and inform himself about the use of these practices, and of the
-company, as responsible for making its customers aware and acting in compliance with government
-laws, for fair and non-discriminatory use.},
-   author = {Fabio Salassa and Paolo Pautassi},
-   school = {Politecnico di Torino},
-   title = {Politecnico di Torino Algorithmic Pricing in the digital age "Ethical considerations on its economic and social implications, and an analysis of possible solutions to overcome its critical issues" Tutor: Candidate},
-   url = {https://webthesis.biblio.polito.it/secure/31375/1/tesi.pdf}
-}
-@inproceedings{Mueller2019,
-   author = {Jonas W Mueller and Vasilis Syrgkanis and Matt Taddy},
-   booktitle = {Advances in Neural Information Processing Systems 32 (NeurIPS 2019)},
-   pages = {15442-15452},
-   title = {Low-Rank Bandit Methods for High-Dimensional Dynamic Pricing},
-   url = {https://proceedings.neurips.cc/paper/2019/file/0a3df70393993583a13c0dd6686f3f32-Paper.pdf},
-   year = {2019}
-}
-@article{Amjad2017,
-   abstract = { In this paper, the question of interest is estimating true demand of a product at a given store location and time period in the retail environment based on a single noisy and potentially censored observation. To address this question, we introduce a %non-parametric framework to make inference from multiple time series. Somewhat surprisingly, we establish that the algorithm introduced for the purpose of "matrix completion" can be used to solve the relevant inference problem. Specifically, using the Universal Singular Value Thresholding (USVT) algorithm [7], we show that our estimator is consistent: the average mean squared error of the estimated average demand with respect to the true average demand goes to 0 as the number of store locations and time intervals increase to $\infty$. We establish naturally appealing properties of the resulting estimator both analytically as well as through a sequence of instructive simulations. Using a real dataset in retail (Walmart), we argue for the practical relevance of our approach. },
-   author = {Muhammad J. Amjad and Devavrat Shah},
-   doi = {10.1145/3154489},
-   issue = {2},
-   journal = {Proceedings of the ACM on Measurement and Analysis of Computing Systems},
-   month = {12},
-   pages = {1-28},
-   publisher = {Association for Computing Machinery (ACM)},
-   title = {Censored Demand Estimation in Retail},
-   volume = {1},
-   url = {https://par.nsf.gov/servlets/purl/10066022},
-   year = {2017}
-}
-@article{Prez-Ricardo2025,
-   abstract = {The study aims to explore tourists' booking intentions by analyzing the price elasticity of demand in tourist accommodations. This analysis should reveal how changes in price affect booking behavior across different customer segments, using online booking records. A dataset was compiled from 106 hotels in Malaga, Spain, comprising 27,910 online bookings sourced exclusively from hotel websites. To understand the price elasticity of demand, a simple log-log regression was applied, segmenting the data based on key revenue-related variables. Subsequently, a cluster segmentation was performed using the Elbow method and K-means algorithm to identify distinct market segments. The findings highlighted that Family Travelers and Short Stay Travelers segments exhibited elastic demand, indicating higher sensitivity to price fluctuations. In contrast, Early Bookers and Mid-Season Long Stayers demonstrated inelastic demand, with lower responsiveness to changes in tourist accommodation prices. The number of variables analyzed in this study, along with the cluster analysis, represent a novelty and contribute to the existing literature on market segmentation and price elasticity of demand. This integration enriches both fields of research, offering mutual benefits and deeper insights that enhance the understanding of booking intention and pricing strategies.},
-   author = {Elizabeth del Carmen Pérez-Ricardo and Josefa García-Mestanza},
-   doi = {10.1016/j.iedeen.2025.100271},
-   issn = {24448834},
-   issue = {1},
-   journal = {European Research on Management and Business Economics},
-   keywords = {Booking intention,Price elasticity,Tourist segmentation},
-   month = {1},
-   publisher = {European Academy of Management and Business Economics},
-   title = {Exploring booking intentions through price elasticity of demand in tourism accommodations using large-scale data analytics},
-   volume = {31},
-   year = {2025}
-}
-@article{Iliou2021,
-   author = {Christos Iliou and Theodoros Kostoulas and Theodora Tsikrika and Vasilis Katos and Stefanos Vrochidis and Ioannis Kompatsiaris},
-   doi = {10.1145/3447815},
-   issue = {3},
-   journal = {Digital Threats: Research and Practice},
-   pages = {1-26},
-   title = {Detection of Advanced Web Bots by Combining Web Logs with Mouse Behavioural Biometrics},
-   volume = {2},
-   url = {https://dl.acm.org/doi/10.1145/3447815},
-   year = {2021}
-}
-@article{ArnoudVdenBoer2015,
-   author = {Arnoud V. den Boer},
-   doi = {10.1016/j.sorms.2015.03.001},
-   issue = {1},
-   journal = {Surveys in Operations Research and Management Science},
-   month = {6},
-   pages = {1-18},
-   title = {Dynamic pricing and learning: Historical origins, current research, and new directions},
-   volume = {20},
-   url = {https://www.sciencedirect.com/science/article/pii/S1876735415000021},
-   year = {2015}
-}
-@article{Calvano2018,
-   author = {Emilio Calvano and Giacomo Calzolari and Vincenzo Denicolo and Sergio Pastorello},
-   doi = {10.2139/ssrn.3304991},
-   journal = {SSRN Electronic Journal},
-   title = {Artificial Intelligence, Algorithmic Pricing and Collusion},
-   url = {https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3304991},
-   year = {2018}
-}

paper/src/main.tex

@@ -10,7 +10,7 @@
 
 \begin{document}
 
-\title{First Proposal: Pricing Heuristics Against Non-human Transaction Orchestration Mechanisms}
+\title{Pricing Heuristics Against Non-human Transaction Orchestration Mechanisms}
 
 \author{Daniel Rösel}
 \email{daniel@alves.world}
@@ -34,60 +34,19 @@ The primary objective of this thesis is to develop and validate pricing heuristi
 
 \maketitle
 
-\section{Preliminary literature review}
-
-From very relevant news, the legal conflicts of agentic access to platforms have clearly indicated a need for prevention of secondary negative effects on ``legacy'' systems which power modern pricing systems \cite{Ghaffary}. Dynamic pricing algorithms rely on directly translating demand features $q$ to $\hat{p}$ new price assignments across a catalogue of products. This demand estimation does often take into account a small degree of error and noise from the data. However, adversarially introduced interactions, which are non-conducive to pricing optimization nor are a fully accurate representation of the driving human demand, have not been considered as part of the systems. Research such as \cite{Mueller2019} introduces very clear methodology for pricing algorithms backed by demand estimation for online pricing optimization which can be followed for proposing adjustments and improvements as highlighted in \ref{research}. Another often encountered demand distortion occurs through censored demand environments \cite{Amjad2017}.
-
-Other efforts such as \cite{Calvano2018} explore ways of modeling the interactions between multiple pricing algorithms or agents which in an effort to maximize their reward drive the market to supra-competitive pricing which leaves the boundaries of the market equilibrium, creating a harmful effect on the customers by this process of algorithmic collusion. This harm can be directly translated to our setting where through interactions between two learners there is a potential of market destabilization.
-
-
-\section{Research question or objective} \label{research}
-
-\begin{quote}
-How do agent-generated interactions contaminate demand functions in dynamic pricing algorithms, and how significantly does this contamination affect key performance indicators ($\Delta$)?
-\end{quote}
-The objectives are to gather data on how humans ($H$) and agents ($A$) interact with commerce platforms, and to identify the most reliable methodology for true demand estimation to fuel the dynamic pricing algorithm. This discrimination task can be accomplished through three distinct approaches:
-
-\begin{enumerate}
-\item \textbf{Explicit filtering approach:} Decompose pipeline components and employ an estimator $P(A|s)$ (where $s$ represents session interaction data) to explicitly filter agent-generated interactions from the processing stream.
-
-\item \textbf{Learned transformation approach:} Utilize a learned transformation on the product demand feature $B$, where $B = B_H + B_A$, with the goal of deriving a more representative demand feature $B_\text{clean} = B_H + W_\epsilon B_A$ that appropriately weights agent contributions.
-
-\item \textbf{Reinforcement learning approach:} Frame the problem as a reinforcement learning task where interactions are modeled as environmental components, guiding the algorithm to learn an appropriate pricing policy that implicitly accounts for genuine human demand ($B_H$).
-\end{enumerate}
-
-
-\section{Execution plan with approximate calendar}
-
-
-This is a tentative execution plan for this research, keeping in mind a more agile approach rather than a waterfall-like set of goals and targets:
-
-\begin{description}
-\item[November 2024:] Complete platform deployment for data collection and observations (70\% complete). Implement user authentication system with magic link invites to enable participant enrollment.
-
-\item[December 2024:] Gather initial interaction data and explore the separability of distributions between human and agentic interaction patterns. Begin testing online algorithms for session-based pricing optimizations.
-
-\item[January 2025:] Conduct controlled experiments comparing human versus agent execution of identical tasks. Establish behavioral signature models and quantify contamination impact ($\Delta$). Develop and validate the explicit filtering approach using $P(A|s)$ estimator.
-
-\item[February 2025:] Design and train the learned transformation model for demand feature adjustment ($B_\text{clean}$). Implement reinforcement learning framework and train pricing policy that implicitly accounts for genuine human demand.
-
-\item[March 2025:] Conduct comparative evaluation across all three proposed approaches. Finalize experimental results and perform statistical analysis of revenue recovery and KPI improvements.
-
-\item[April 2025:] Internal review, revisions, and thesis documentation finalization. Prepare for final submission.
-\end{description}
-
-\section{Desired measurable outcome or answer}
-
-The first step is measuring how well we can separate human from agent session data. We can start with standard accuracy metrics as a baseline.
-What really matters for the larger picture is the economic impact of accurate demand estimation. We measure this through revenue leakage and revenue recovery. For benchmarking, we need to compare scenarios under default pricing policies versus adjusted ones - this gives us lower and upper bounds for our performance.
-Since we're also concerned with human-centric outcomes, we need to collect user friction ratings that compare more radical solutions (like CAPTCHAs) against minimal or no defenses.
+\input{chapters/01-intro}
+\input{chapters/02-literature-review}
+\input{chapters/03-methodology}
+\input{chapters/04-results}
+\input{chapters/05-discussion}
+\input{chapters/06-conclusion}
 
 
 \printbibliography
 
-% \clearpage
-% \onecolumn
-% \appendix
-
+\clearpage
+\onecolumn
+\appendix
+\input{../build/concatenated_code}
 
 \end{document}

pytest.ini

@@ -0,0 +1,8 @@
+[pytest]
+pythonpath = experiments
+testpaths = experiments
+python_files = test*.py
+python_classes = Test*
+python_functions = test_*
+asyncio_mode = auto
+asyncio_default_fixture_loop_scope = function

requirements.txt

@@ -5,3 +5,9 @@ jupyter
 ipykernel
 matplotlib
 graphviz
+browser-use
+pytest
+pytest-asyncio
+uv
+scikit-learn
+supabase

web/package-lock.json

@@ -8,6 +8,8 @@
       "name": "web",
       "version": "0.1.0",
       "dependencies": {
+        "@supabase/ssr": "^0.7.0",
+        "@supabase/supabase-js": "^2.81.1",
         "next": "16.0.0",
         "react": "19.2.0",
         "react-dom": "19.2.0",
@@ -657,6 +659,97 @@
         "node": ">= 10"
       }
     },
+    "node_modules/@supabase/auth-js": {
+      "version": "2.81.1",
+      "resolved": "https://registry.npmjs.org/@supabase/auth-js/-/auth-js-2.81.1.tgz",
+      "integrity": "sha512-K20GgiSm9XeRLypxYHa5UCnybWc2K0ok0HLbqCej/wRxDpJxToXNOwKt0l7nO8xI1CyQ+GrNfU6bcRzvdbeopQ==",
+      "license": "MIT",
+      "dependencies": {
+        "tslib": "2.8.1"
+      },
+      "engines": {
+        "node": ">=20.0.0"
+      }
+    },
+    "node_modules/@supabase/functions-js": {
+      "version": "2.81.1",
+      "resolved": "https://registry.npmjs.org/@supabase/functions-js/-/functions-js-2.81.1.tgz",
+      "integrity": "sha512-sYgSO3mlgL0NvBFS3oRfCK4OgKGQwuOWJLzfPyWg0k8MSxSFSDeN/JtrDJD5GQrxskP6c58+vUzruBJQY78AqQ==",
+      "license": "MIT",
+      "dependencies": {
+        "tslib": "2.8.1"
+      },
+      "engines": {
+        "node": ">=20.0.0"
+      }
+    },
+    "node_modules/@supabase/postgrest-js": {
+      "version": "2.81.1",
+      "resolved": "https://registry.npmjs.org/@supabase/postgrest-js/-/postgrest-js-2.81.1.tgz",
+      "integrity": "sha512-DePpUTAPXJyBurQ4IH2e42DWoA+/Qmr5mbgY4B6ZcxVc/ZUKfTVK31BYIFBATMApWraFc8Q/Sg+yxtfJ3E0wSg==",
+      "license": "MIT",
+      "dependencies": {
+        "tslib": "2.8.1"
+      },
+      "engines": {
+        "node": ">=20.0.0"
+      }
+    },
+    "node_modules/@supabase/realtime-js": {
+      "version": "2.81.1",
+      "resolved": "https://registry.npmjs.org/@supabase/realtime-js/-/realtime-js-2.81.1.tgz",
+      "integrity": "sha512-ViQ+Kxm8BuUP/TcYmH9tViqYKGSD1LBjdqx2p5J+47RES6c+0QHedM0PPAjthMdAHWyb2LGATE9PD2++2rO/tw==",
+      "license": "MIT",
+      "dependencies": {
+        "@types/phoenix": "^1.6.6",
+        "@types/ws": "^8.18.1",
+        "tslib": "2.8.1",
+        "ws": "^8.18.2"
+      },
+      "engines": {
+        "node": ">=20.0.0"
+      }
+    },
+    "node_modules/@supabase/ssr": {
+      "version": "0.7.0",
+      "resolved": "https://registry.npmjs.org/@supabase/ssr/-/ssr-0.7.0.tgz",
+      "integrity": "sha512-G65t5EhLSJ5c8hTCcXifSL9Q/ZRXvqgXeNo+d3P56f4U1IxwTqjB64UfmfixvmMcjuxnq2yGqEWVJqUcO+AzAg==",
+      "license": "MIT",
+      "dependencies": {
+        "cookie": "^1.0.2"
+      },
+      "peerDependencies": {
+        "@supabase/supabase-js": "^2.43.4"
+      }
+    },
+    "node_modules/@supabase/storage-js": {
+      "version": "2.81.1",
+      "resolved": "https://registry.npmjs.org/@supabase/storage-js/-/storage-js-2.81.1.tgz",
+      "integrity": "sha512-UNmYtjnZnhouqnbEMC1D5YJot7y0rIaZx7FG2Fv8S3hhNjcGVvO+h9We/tggi273BFkiahQPS/uRsapo1cSapw==",
+      "license": "MIT",
+      "dependencies": {
+        "tslib": "2.8.1"
+      },
+      "engines": {
+        "node": ">=20.0.0"
+      }
+    },
+    "node_modules/@supabase/supabase-js": {
+      "version": "2.81.1",
+      "resolved": "https://registry.npmjs.org/@supabase/supabase-js/-/supabase-js-2.81.1.tgz",
+      "integrity": "sha512-KSdY7xb2L0DlLmlYzIOghdw/na4gsMcqJ8u4sD6tOQJr+x3hLujU9s4R8N3ob84/1bkvpvlU5PYKa1ae+OICnw==",
+      "license": "MIT",
+      "dependencies": {
+        "@supabase/auth-js": "2.81.1",
+        "@supabase/functions-js": "2.81.1",
+        "@supabase/postgrest-js": "2.81.1",
+        "@supabase/realtime-js": "2.81.1",
+        "@supabase/storage-js": "2.81.1"
+      },
+      "engines": {
+        "node": ">=20.0.0"
+      }
+    },
     "node_modules/@swc/helpers": {
       "version": "0.5.15",
       "resolved": "https://registry.npmjs.org/@swc/helpers/-/helpers-0.5.15.tgz",
@@ -941,12 +1034,17 @@
       "version": "20.19.23",
       "resolved": "https://registry.npmjs.org/@types/node/-/node-20.19.23.tgz",
       "integrity": "sha512-yIdlVVVHXpmqRhtyovZAcSy0MiPcYWGkoO4CGe/+jpP0hmNuihm4XhHbADpK++MsiLHP5MVlv+bcgdF99kSiFQ==",
-      "dev": true,
       "license": "MIT",
       "dependencies": {
         "undici-types": "~6.21.0"
       }
     },
+    "node_modules/@types/phoenix": {
+      "version": "1.6.6",
+      "resolved": "https://registry.npmjs.org/@types/phoenix/-/phoenix-1.6.6.tgz",
+      "integrity": "sha512-PIzZZlEppgrpoT2QgbnDU+MMzuR6BbCjllj0bM70lWoejMeNJAxCchxnv7J3XFkI8MpygtRpzXrIlmWUBclP5A==",
+      "license": "MIT"
+    },
     "node_modules/@types/react": {
       "version": "19.2.2",
       "resolved": "https://registry.npmjs.org/@types/react/-/react-19.2.2.tgz",
@@ -967,6 +1065,15 @@
         "@types/react": "^19.2.0"
       }
     },
+    "node_modules/@types/ws": {
+      "version": "8.18.1",
+      "resolved": "https://registry.npmjs.org/@types/ws/-/ws-8.18.1.tgz",
+      "integrity": "sha512-ThVF6DCVhA8kUGy+aazFQ4kXQ7E1Ty7A3ypFOe0IcJV8O/M511G99AW24irKrW56Wt44yG9+ij8FaqoBGkuBXg==",
+      "license": "MIT",
+      "dependencies": {
+        "@types/node": "*"
+      }
+    },
     "node_modules/caniuse-lite": {
       "version": "1.0.30001751",
       "resolved": "https://registry.npmjs.org/caniuse-lite/-/caniuse-lite-1.0.30001751.tgz",
@@ -993,6 +1100,15 @@
       "integrity": "sha512-IV3Ou0jSMzZrd3pZ48nLkT9DA7Ag1pnPzaiQhpW7c3RbcqqzvzzVu+L8gfqMp/8IM2MQtSiqaCxrrcfu8I8rMA==",
       "license": "MIT"
     },
+    "node_modules/cookie": {
+      "version": "1.0.2",
+      "resolved": "https://registry.npmjs.org/cookie/-/cookie-1.0.2.tgz",
+      "integrity": "sha512-9Kr/j4O16ISv8zBBhJoi4bXOYNTkFLOqSL3UDB0njXxCXNezjeyVrJyGOWtgfs/q2km1gwBcfH8q1yEGoMYunA==",
+      "license": "MIT",
+      "engines": {
+        "node": ">=18"
+      }
+    },
     "node_modules/csstype": {
       "version": "3.1.3",
       "resolved": "https://registry.npmjs.org/csstype/-/csstype-3.1.3.tgz",
@@ -1605,9 +1721,29 @@
       "version": "6.21.0",
       "resolved": "https://registry.npmjs.org/undici-types/-/undici-types-6.21.0.tgz",
       "integrity": "sha512-iwDZqg0QAGrg9Rav5H4n0M64c3mkR59cJ6wQp+7C4nI0gsmExaedaYLNO44eT4AtBBwjbTiGPMlt2Md0T9H9JQ==",
-      "dev": true,
       "license": "MIT"
     },
+    "node_modules/ws": {
+      "version": "8.18.3",
+      "resolved": "https://registry.npmjs.org/ws/-/ws-8.18.3.tgz",
+      "integrity": "sha512-PEIGCY5tSlUt50cqyMXfCzX+oOPqN0vuGqWzbcJ2xvnkzkq46oOpz7dQaTDBdfICb4N14+GARUDw2XV2N4tvzg==",
+      "license": "MIT",
+      "engines": {
+        "node": ">=10.0.0"
+      },
+      "peerDependencies": {
+        "bufferutil": "^4.0.1",
+        "utf-8-validate": ">=5.0.2"
+      },
+      "peerDependenciesMeta": {
+        "bufferutil": {
+          "optional": true
+        },
+        "utf-8-validate": {
+          "optional": true
+        }
+      }
+    },
     "node_modules/zod": {
       "version": "4.1.12",
       "resolved": "https://registry.npmjs.org/zod/-/zod-4.1.12.tgz",

web/package.json

@@ -8,6 +8,8 @@
     "start": "next start"
   },
   "dependencies": {
+    "@supabase/ssr": "^0.7.0",
+    "@supabase/supabase-js": "^2.81.1",
     "next": "16.0.0",
     "react": "19.2.0",
     "react-dom": "19.2.0",

web/src/app/admin/experiments/page.tsx

@@ -1,20 +1,26 @@
 'use client';
 
 import { useEffect, useState } from 'react';
-import { useSession } from '@/hooks/useSession';
+import { TaskManager } from '@/components/admin/TaskManager';
+import { ExperimentForm } from '@/components/admin/ExperimentForm';
 
 type Experiment = {
   id: string;
-  status: 'active' | 'stopped';
-  sessionIds: string[];
-  createdAt: number;
+  subject_name: string;
+  xp_human_only: boolean;
+  xp_market_mode: string;
+  created_at: string;
+  task?: {
+    id: string;
+    task_name: string;
+  };
 };
 
 export default function ExperimentsAdmin() {
-  const { sessionId, isLoading: sessionLoading } = useSession();
   const [exps, setExps] = useState<Experiment[]>([]);
-  const [loading, setLoading] = useState(false);
+  const [selectedTaskId, setSelectedTaskId] = useState<string | undefined>();
   const [error, setError] = useState<string | null>(null);
+  const [showForm, setShowForm] = useState(false);
 
   const fetchExps = async () => {
     try {
@@ -31,86 +37,22 @@ export default function ExperimentsAdmin() {
     fetchExps();
   }, []);
 
-  const handleStart = async () => {
-    if (!sessionId) {
-      setError('no session available');
-      return;
-    }
-
-    setLoading(true);
-    setError(null);
-
-    try {
-      const res = await fetch('/api/admin/experiments/start', {
-        method: 'POST',
-        headers: { 'Content-Type': 'application/json' },
-        body: JSON.stringify({ sessionId }),
-      });
-
-      if (!res.ok) {
-        const data = await res.json();
-        throw new Error(data.error || 'start failed');
-      }
-
-      await fetchExps(); // refresh list
-    } catch (err: any) {
-      setError(err.message);
-    } finally {
-      setLoading(false);
-    }
+  const handleExperimentCreated = async () => {
+    setShowForm(false);
+    setSelectedTaskId(undefined);
+    await fetchExps();
   };
 
-  const handleStop = async (expId: string) => {
-    setLoading(true);
-    setError(null);
-
-    try {
-      const res = await fetch('/api/admin/experiments/stop', {
-        method: 'POST',
-        headers: { 'Content-Type': 'application/json' },
-        body: JSON.stringify({ experimentId: expId }),
-      });
-
-      if (!res.ok) {
-        const data = await res.json();
-        throw new Error(data.error || 'stop failed');
-      }
-
-      await fetchExps(); // refresh list
-    } catch (err: any) {
-      setError(err.message);
-    } finally {
-      setLoading(false);
-    }
-  };
-
-  if (sessionLoading) {
-    return (
-      <div className="flex min-h-screen items-center justify-center bg-zinc-50 dark:bg-black">
-        <p className="text-zinc-600 dark:text-zinc-400">loading session...</p>
-      </div>
-    );
-  }
-
   return (
     <div className="min-h-screen bg-zinc-50 px-6 py-12 dark:bg-black">
-      <div className="mx-auto max-w-5xl">
-        <div className="mb-8 flex items-center justify-between">
-          <div>
-            <h1 className="text-3xl font-semibold tracking-tight text-black dark:text-zinc-50">
-              Experiments
-            </h1>
-            <p className="mt-2 text-sm text-zinc-600 dark:text-zinc-400">
-              current session: {sessionId || 'none'}
-            </p>
-          </div>
-          <button
-            onClick={handleStart}
-            disabled={loading || !sessionId}
-            className="rounded-lg bg-black px-4 py-2 text-sm font-medium text-white transition-colors hover:bg-zinc-800 disabled:opacity-50 dark:bg-zinc-50 dark:text-black dark:hover:bg-zinc-200"
-          >
-            {loading ? 'starting...' : 'start experiment'}
-          </button>
+      <div className="mx-auto max-w-7xl">
+        <div className="mb-8">
+          <h1 className="text-3xl font-semibold tracking-tight text-black dark:text-zinc-50">
+            Experiment Management
+          </h1>
+          <p className="mt-2 text-sm text-zinc-600 dark:text-zinc-400">
+            configure tasks and run experiments
+          </p>
         </div>
 
         {error && (
@@ -119,79 +61,123 @@ export default function ExperimentsAdmin() {
           </div>
         )}
 
-        <div className="overflow-hidden rounded-lg border border-zinc-200 bg-white dark:border-zinc-800 dark:bg-zinc-950">
-          <table className="w-full text-left text-sm">
-            <thead className="border-b border-zinc-200 bg-zinc-50 dark:border-zinc-800 dark:bg-zinc-900">
-              <tr>
-                <th className="px-6 py-3 font-medium text-zinc-900 dark:text-zinc-100">
-                  experiment id
-                </th>
-                <th className="px-6 py-3 font-medium text-zinc-900 dark:text-zinc-100">
-                  status
-                </th>
-                <th className="px-6 py-3 font-medium text-zinc-900 dark:text-zinc-100">
-                  session count
-                </th>
-                <th className="px-6 py-3 font-medium text-zinc-900 dark:text-zinc-100">
-                  created
-                </th>
-                <th className="px-6 py-3 font-medium text-zinc-900 dark:text-zinc-100">
-                  action
-                </th>
-              </tr>
-            </thead>
-            <tbody className="divide-y divide-zinc-200 dark:divide-zinc-800">
-              {exps.length === 0 ? (
-                <tr>
-                  <td
-                    colSpan={5}
-                    className="px-6 py-8 text-center text-zinc-500 dark:text-zinc-400"
-                  >
-                    no experiments yet
-                  </td>
-                </tr>
-              ) : (
-                exps.map((exp) => (
-                  <tr
-                    key={exp.id}
-                    className="hover:bg-zinc-50 dark:hover:bg-zinc-900"
-                  >
-                    <td className="px-6 py-4 font-mono text-xs text-zinc-700 dark:text-zinc-300">
-                      {exp.id.slice(0, 8)}...
-                    </td>
-                    <td className="px-6 py-4">
-                      <span
-                        className={`inline-block rounded-full px-2 py-1 text-xs font-medium ${
-                          exp.status === 'active'
-                            ? 'bg-green-100 text-green-800 dark:bg-green-950 dark:text-green-200'
-                            : 'bg-zinc-100 text-zinc-800 dark:bg-zinc-800 dark:text-zinc-200'
-                        }`}
-                      >
-                        {exp.status}
-                      </span>
-                    </td>
-                    <td className="px-6 py-4 text-zinc-700 dark:text-zinc-300">
-                      {exp.sessionIds.length}
-                    </td>
-                    <td className="px-6 py-4 text-zinc-700 dark:text-zinc-300">
-                      {new Date(exp.createdAt).toLocaleString()}
-                    </td>
-                    <td className="px-6 py-4">
-                      {exp.status === 'active' && (
-                        <button
-                          onClick={() => handleStop(exp.id)}
-                          disabled={loading}
-                          className="text-sm font-medium text-red-600 hover:text-red-700 disabled:opacity-50 dark:text-red-400 dark:hover:text-red-300"
-                        >
-                          stop
-                        </button>
-                      )}
-                    </td>
+        <div className="grid grid-cols-1 gap-6 lg:grid-cols-3">
+          {/* left column: task manager */}
+          <div className="lg:col-span-1">
+            <TaskManager
+              onTaskSelect={setSelectedTaskId}
+              selectedTaskId={selectedTaskId}
+            />
+          </div>
+
+          {/* right column: experiment form + list */}
+          <div className="space-y-6 lg:col-span-2">
+            <div className="flex items-center justify-between">
+              <h2 className="text-lg font-semibold text-zinc-900 dark:text-zinc-100">
+                Experiments
+              </h2>
+              <button
+                onClick={() => setShowForm(!showForm)}
+                className="rounded-lg bg-black px-4 py-2 text-sm font-medium text-white transition-colors hover:bg-zinc-800 dark:bg-zinc-50 dark:text-black dark:hover:bg-zinc-200"
+              >
+                {showForm ? 'hide form' : 'new experiment'}
+              </button>
+            </div>
+
+            {showForm && (
+              <ExperimentForm
+                selectedTaskId={selectedTaskId}
+                onSuccess={handleExperimentCreated}
+              />
+            )}
+
+            <div className="overflow-hidden rounded-lg border border-zinc-200 bg-white dark:border-zinc-800 dark:bg-zinc-950">
+              <table className="w-full text-left text-sm">
+                <thead className="border-b border-zinc-200 bg-zinc-50 dark:border-zinc-800 dark:bg-zinc-900">
+                  <tr>
+                    <th className="px-4 py-3 font-medium text-zinc-900 dark:text-zinc-100">
+                      subject
+                    </th>
+                    <th className="px-4 py-3 font-medium text-zinc-900 dark:text-zinc-100">
+                      mode
+                    </th>
+                    <th className="px-4 py-3 font-medium text-zinc-900 dark:text-zinc-100">
+                      human
+                    </th>
+                    <th className="px-4 py-3 font-medium text-zinc-900 dark:text-zinc-100">
+                      task
+                    </th>
+                    <th className="px-4 py-3 font-medium text-zinc-900 dark:text-zinc-100">
+                      created
+                    </th>
+                    <th className="px-4 py-3 font-medium text-zinc-900 dark:text-zinc-100">
+                      link
+                    </th>
                   </tr>
-                ))
-              )}
-            </tbody>
-          </table>
+                </thead>
+                <tbody className="divide-y divide-zinc-200 dark:divide-zinc-800">
+                  {exps.length === 0 ? (
+                    <tr>
+                      <td
+                        colSpan={6}
+                        className="px-4 py-8 text-center text-zinc-500 dark:text-zinc-400"
+                      >
+                        no experiments yet
+                      </td>
+                    </tr>
+                  ) : (
+                    exps.map((exp) => {
+                      const baseUrl = exp.xp_market_mode === 'airline'
+                        ? 'https://phantom-airline.vercel.app'
+                        : 'https://phantom-hotel.vercel.app';
+                      const link = `${baseUrl}/start-task?uuid=${exp.id}`;
+
+                      return (
+                        <tr
+                          key={exp.id}
+                          className="hover:bg-zinc-50 dark:hover:bg-zinc-900"
+                        >
+                          <td className="px-4 py-3 font-medium text-zinc-900 dark:text-zinc-100">
+                            {exp.subject_name}
+                          </td>
+                          <td className="px-4 py-3">
+                            <span className="inline-block rounded-full bg-zinc-100 px-2 py-1 text-xs font-medium text-zinc-800 dark:bg-zinc-800 dark:text-zinc-200">
+                              {exp.xp_market_mode || 'none'}
+                            </span>
+                          </td>
+                          <td className="px-4 py-3">
+                            {exp.xp_human_only ? (
+                              <span className="text-xs text-green-600 dark:text-green-400">
+                                yes
+                              </span>
+                            ) : (
+                              <span className="text-xs text-zinc-500">no</span>
+                            )}
+                          </td>
+                          <td className="px-4 py-3 text-xs text-zinc-600 dark:text-zinc-400">
+                            {exp.task ? exp.task.task_name : '—'}
+                          </td>
+                          <td className="px-4 py-3 text-xs text-zinc-600 dark:text-zinc-400">
+                            {new Date(exp.created_at).toLocaleDateString()}
+                          </td>
+                          <td className="px-4 py-3">
+                            <button
+                              onClick={() => {
+                                navigator.clipboard.writeText(link);
+                              }}
+                              className="text-xs font-medium text-zinc-900 hover:text-zinc-600 dark:text-zinc-100 dark:hover:text-zinc-400"
+                            >
+                              copy link
+                            </button>
+                          </td>
+                        </tr>
+                      );
+                    })
+                  )}
+                </tbody>
+              </table>
+            </div>
+          </div>
         </div>
       </div>
     </div>

web/src/app/airline/products/[id]/page.tsx

@@ -0,0 +1,106 @@
+'use client';
+
+import { useState, useEffect } from 'react';
+import { useParams, useRouter } from 'next/navigation';
+import { Navigation } from '@/components/ui';
+import { useCart } from '@/contexts/CartContext';
+import AirlineDetails from '@/components/feats/airline/AirlineDetails';
+import { transformProduct, type Flight, type AirlineProduct } from '@/lib/airline-utils';
+import type { EventName } from '@/lib/events';
+
+const dispatchInteraction = (eventName: EventName, productId?: string, metadata?: Record<string, unknown>) => {
+    const e = new CustomEvent('definedInteraction', {
+        detail: { eventName, productId, metadata },
+    });
+    document.dispatchEvent(e);
+};
+
+export default function AirlineProductPage() {
+    const params = useParams();
+    const router = useRouter();
+    const { addItem } = useCart();
+    const [product, setProduct] = useState<Flight | null>(null);
+    const [loading, setLoading] = useState(true);
+    const [error, setError] = useState<string | null>(null);
+    const [added, setAdded] = useState(false);
+
+    const productId = params.id as string;
+
+    useEffect(() => {
+        const fetchProduct = async () => {
+            try {
+                const res = await fetch(`/api/products/${productId}`);
+                if (!res.ok) throw new Error(`Failed to fetch: ${res.status}`);
+                const json = await res.json();
+                const transformed = transformProduct(json.data as AirlineProduct);
+                setProduct(transformed);
+
+                // fire learn_more_about_item event when product loads
+                dispatchInteraction('learn_more_about_item', productId, {
+                    type: 'airline',
+                    dateIndex: transformed.dateIndex,
+                    flightType: transformed.flightType,
+                });
+            } catch (e) {
+                setError(e instanceof Error ? e.message : 'Failed to load product');
+                console.error('[FETCH_FLIGHT_ERROR]', e);
+            } finally {
+                setLoading(false);
+            }
+        };
+        fetchProduct();
+    }, [productId]);
+
+    const handleAddToCart = () => {
+        if (!product) return;
+
+        addItem({
+            id: productId,
+            type: 'airline',
+            name: product.flightType,
+            price: product.basePrice,
+            metadata: {
+                departure: product.departure,
+                arrival: product.arrival,
+                duration: product.duration,
+                cabinClass: product.cabinClass,
+            },
+            dateIndex: product.dateIndex,
+        });
+
+        dispatchInteraction('add_item_to_cart', productId, {
+            type: 'airline',
+            price: product.basePrice,
+        });
+
+        setAdded(true);
+        setTimeout(() => setAdded(false), 2000);
+    };
+
+    return (
+        <>
+            <Navigation />
+            <main className="max-w-4xl mx-auto px-4 py-8">
+                {loading && <div className="text-center py-8">Loading flight details...</div>}
+                {error && <div className="text-red-500 text-center py-8">{error}</div>}
+
+                {!loading && !error && product && (
+                    <>
+                        <button
+                            onClick={() => router.back()}
+                            className="mt-6 text-blue-600 hover:underline"
+                        >
+                            ← Back to flights
+                        </button>
+                        <AirlineDetails
+                            product={product}
+                            onAddToCart={handleAddToCart}
+                            addedToCart={added}
+                        />
+
+                    </>
+                )}
+            </main>
+        </>
+    );
+}

web/src/app/airline/products/page.tsx

@@ -1,73 +1,69 @@
 'use client';
 
+import { useState, useEffect, Suspense } from 'react';
+import { useSearchParams } from 'next/navigation';
 import { Navigation } from '@/components/ui';
 import AirlineCard from '@/components/feats/airline/AirlineCard';
+import { transformProduct, type Flight, type AirlineProduct } from '@/lib/airline-utils';
 
-type CabinClass = 'economy' | 'premium' | 'business' | 'first';
-type FareRule = 'flexible' | 'standard' | 'basic';
+function FlightsList() {
+  const searchParams = useSearchParams();
+  const [flights, setFlights] = useState<Flight[]>([]);
+  const [loading, setLoading] = useState(true);
+  const [error, setError] = useState<string | null>(null);
 
-interface Flight {
-  id: string;
-  departure: { time: string; airport: string };
-  arrival: { time: string; airport: string };
-  duration: string;
-  stops: number;
-  cabinClass: CabinClass;
-  fareRule: FareRule;
-  refundable: boolean;
-  basePrice: number;
-}
+  useEffect(() => {
+    const fetchFlights = async () => {
+      try {
+        const url = new URL('/api/products', window.location.origin);
+        url.searchParams.set('type', 'airline');
 
-const genRandomFlights = (): Flight[] => {
-  const airports = ['JFK', 'LAX', 'ORD', 'ATL', 'DFW', 'SFO', 'SEA', 'MIA'];
-  const cabins: CabinClass[] = ['economy', 'premium', 'business', 'first'];
-  const fareRules: FareRule[] = ['flexible', 'standard', 'basic'];
+        // forward all relevant search params to the API
+        const params = ['dateIndex', 'origin', 'destination', 'tripType', 'adults', 'children', 'infants'];
+        params.forEach(param => {
+          const val = searchParams.get(param);
+          if (val) url.searchParams.set(param, val);
+        });
 
-  return Array.from({ length: 12 }, (_, i) => {
-    const depHour = Math.floor(Math.random() * 24);
-    const arrHour = (depHour + Math.floor(Math.random() * 6) + 2) % 24;
-    const stops = Math.random() > 0.6 ? 0 : Math.floor(Math.random() * 2) + 1;
-    const cabin = cabins[Math.floor(Math.random() * cabins.length)];
-    const fareRule = fareRules[Math.floor(Math.random() * fareRules.length)];
-
-    const basePrice = Math.floor(
-      (cabin === 'economy' ? 200 : cabin === 'premium' ? 400 : cabin === 'business' ? 800 : 1500) +
-      Math.random() * 300
-    );
-
-    return {
-      id: `flt-${i}`,
-      departure: {
-        time: `${depHour.toString().padStart(2, '0')}:${Math.floor(Math.random() * 60).toString().padStart(2, '0')}`,
-        airport: airports[Math.floor(Math.random() * airports.length)],
-      },
-      arrival: {
-        time: `${arrHour.toString().padStart(2, '0')}:${Math.floor(Math.random() * 60).toString().padStart(2, '0')}`,
-        airport: airports[Math.floor(Math.random() * airports.length)],
-      },
-      duration: `${Math.floor(Math.random() * 5) + 2}h ${Math.floor(Math.random() * 60)}m`,
-      stops,
-      cabinClass: cabin,
-      fareRule,
-      refundable: Math.random() > 0.7,
-      basePrice,
+        const res = await fetch(url.toString());
+        if (!res.ok) throw new Error(`Failed to fetch: ${res.status}`);
+        const json = await res.json();
+        const transformed = json.data.map((p: AirlineProduct) => transformProduct(p));
+        setFlights(transformed);
+      } catch (e) {
+        setError(e instanceof Error ? e.message : 'Failed to load products');
+        console.error('[FETCH_ERROR]', e);
+      } finally {
+        setLoading(false);
+      }
     };
-  });
-};
-
-export default function AirlineProducts() {
-  const flights = genRandomFlights();
+    fetchFlights();
+  }, [searchParams]);
 
   return (
     <>
-      <Navigation />
-      <main className="max-w-7xl mx-auto px-4 py-8">
-        <h1 className="text-3xl font-bold mb-6">Available Flights</h1>
+      <h1 className="text-3xl font-bold mb-6">Available Flights</h1>
+      {loading && <div className="text-center py-8">Loading...</div>}
+      {error && <div className="text-red-500 text-center py-8">{error}</div>}
+      {!loading && !error && (
         <div className="space-y-4">
           {flights.map((f) => (
             <AirlineCard key={f.id} flight={f} />
           ))}
         </div>
+      )}
+    </>
+  );
+}
+
+export default function AirlineProducts() {
+  return (
+    <>
+      <Navigation />
+      <main className="max-w-7xl mx-auto px-4 py-8">
+        <Suspense fallback={<div className="text-center py-8">Loading...</div>}>
+          <FlightsList />
+        </Suspense>
       </main>
     </>
   );

web/src/app/api/admin/experiments/route.ts

@@ -1,10 +1,40 @@
-import { NextResponse } from 'next/server';
-import { getAllExperiments } from '@/lib/sessionStore';
+import { NextRequest, NextResponse } from 'next/server';
+import { createClient } from '@/utils/supabase/server';
+import { cookies } from 'next/headers';
 
-export async function GET() {
+export async function GET(req: NextRequest) {
   try {
-    const exps = getAllExperiments();
-    return NextResponse.json({ experiments: exps });
+    const cookieStore = await cookies();
+    const supabase = createClient(cookieStore);
+
+    const { searchParams } = new URL(req.url);
+    const id = searchParams.get('id');
+
+    if (id) {
+      const { data, error } = await supabase
+        .from('experiments')
+        .select(`
+          *,
+          task:tasks(*)
+        `)
+        .eq('id', id)
+        .single();
+
+      if (error) throw error;
+      return NextResponse.json({ experiment: data });
+    }
+
+    const { data, error } = await supabase
+      .from('experiments')
+      .select(`
+        *,
+        task:tasks(*)
+      `)
+      .order('created_at', { ascending: false });
+
+    if (error) throw error;
+
+    return NextResponse.json({ experiments: data || [] });
   } catch (err: any) {
     console.error('experiments list error:', err);
     return NextResponse.json(
@@ -13,3 +43,44 @@ export async function GET() {
     );
   }
 }
+
+export async function POST(req: NextRequest) {
+  try {
+    const cookieStore = await cookies();
+    const supabase = createClient(cookieStore);
+    const body = await req.json();
+
+    const { subject_name, xp_human_only, xp_market_mode, xp_task_id } = body;
+
+    if (!subject_name) {
+      return NextResponse.json(
+        { error: 'subject_name is required' },
+        { status: 400 }
+      );
+    }
+
+    const { data, error } = await supabase
+      .from('experiments')
+      .insert([{
+        subject_name,
+        xp_human_only: xp_human_only ?? false,
+        xp_market_mode: xp_market_mode || null,
+        xp_task_id: xp_task_id || null,
+      }])
+      .select(`
+        *,
+        task:tasks(*)
+      `)
+      .single();
+
+    if (error) throw error;
+
+    return NextResponse.json({ experiment: data });
+  } catch (err: any) {
+    console.error('experiment creation error:', err);
+    return NextResponse.json(
+      { error: err.message || 'unknown error' },
+      { status: 500 }
+    );
+  }
+}

web/src/app/api/admin/tasks/route.ts

@@ -0,0 +1,58 @@
+import { NextRequest, NextResponse } from 'next/server';
+import { createClient } from '@/utils/supabase/server';
+import { cookies } from 'next/headers';
+
+export async function GET() {
+  try {
+    const cookieStore = await cookies();
+    const supabase = createClient(cookieStore);
+
+    const { data, error } = await supabase
+      .from('tasks')
+      .select('*')
+      .order('created_at', { ascending: false });
+
+    if (error) throw error;
+
+    return NextResponse.json({ tasks: data || [] });
+  } catch (err: any) {
+    console.error('tasks fetch error:', err);
+    return NextResponse.json(
+      { error: err.message || 'unknown error' },
+      { status: 500 }
+    );
+  }
+}
+
+export async function POST(req: NextRequest) {
+  try {
+    const cookieStore = await cookies();
+    const supabase = createClient(cookieStore);
+    const body = await req.json();
+
+    const { task_name, task_description, task_def_of_done } = body;
+
+    if (!task_name) {
+      return NextResponse.json(
+        { error: 'task_name is required' },
+        { status: 400 }
+      );
+    }
+
+    const { data, error } = await supabase
+      .from('tasks')
+      .insert([{ task_name, task_description, task_def_of_done }])
+      .select()
+      .single();
+
+    if (error) throw error;
+
+    return NextResponse.json({ task: data });
+  } catch (err: any) {
+    console.error('task creation error:', err);
+    return NextResponse.json(
+      { error: err.message || 'unknown error' },
+      { status: 500 }
+    );
+  }
+}

web/src/app/api/pricing/route.ts

@@ -13,17 +13,6 @@ export async function GET(req: NextRequest) {
     const experimentId = searchParams.get('experimentId');
     const storeMode = process.env.NEXT_PUBLIC_STORE_MODE || 'shop';
 
-    // log in dev
-    if (process.env.NODE_ENV === 'development') {
-        console.log('[pricing-api]', {
-            productId,
-            sessionId,
-            experimentId,
-            storeMode,
-            timestamp: new Date().toISOString(),
-        });
-    }
-
     if (!productId) {
         return NextResponse.json(
             { error: 'productId is required' },
@@ -31,14 +20,73 @@ export async function GET(req: NextRequest) {
         );
     }
 
-    // stub: call external pricing provider (random for now)
-    const basePrice = 100 + Math.random() * 900; // 100-1000 range
-    const price = Math.round(basePrice * 100) / 100;
+    const timestamp = new Date().toISOString();
+    let price: number;
+    let basePrice: number | undefined;
+    let markup: number | undefined;
+    let elasticity: number | undefined;
+
+    // call real pricing provider
+    const providerUrl = process.env.PRICING_PROVIDER_URL || 'http://localhost:5001';
+    try {
+        const queryParams = new URLSearchParams();
+        if (sessionId) queryParams.append('sessionId', sessionId);
+        if (experimentId) queryParams.append('experimentId', experimentId);
+
+        const providerResponse = await fetch(
+            `${providerUrl}/api/${storeMode}/price/${productId}?${queryParams.toString()}`,
+            { headers: { 'Accept': 'application/json' }, cache: 'no-store' }
+        );
+
+        if (!providerResponse.ok) {
+            throw new Error(`Provider returned ${providerResponse.status}`);
+        }
+
+        const providerData = await providerResponse.json();
+        price = providerData.price;
+        basePrice = providerData.base_price;
+        markup = providerData.markup;
+        elasticity = providerData.elasticity;
+
+    } catch (err) {
+        console.error('[pricing-provider-error]', err);
+        // fallback to random pricing if provider unavailable
+        const randomBase = 100 + Math.random() * 900;
+        price = Math.round(randomBase * 100) / 100;
+    }
+
+    // log price to kafka for elasticity computation
+    if (sessionId) {
+        const backendUrl = process.env.BACKEND_URL || 'http://localhost:5000';
+        try {
+            await fetch(`${backendUrl}/api/kafka/price-log`, {
+                method: 'POST',
+                headers: { 'Content-Type': 'application/json' },
+                body: JSON.stringify({
+                    productId,
+                    price,
+                    sessionId,
+                    experimentId: experimentId || undefined,
+                    storeMode,
+                    ts: timestamp,
+                }),
+            });
+        } catch (err) {
+            console.error('[price-log-error]', err);
+        }
+    }
+
+    if (process.env.NODE_ENV === 'development') {
+        console.log('[pricing-api]', {
+            productId, sessionId, experimentId, storeMode,
+            price, basePrice, markup, elasticity, timestamp,
+        });
+    }
 
     const response: PricingResponse = {
         price,
         currency: 'EUR',
-        cachedAt: new Date().toISOString(),
+        cachedAt: timestamp,
     };
 
     return NextResponse.json(response);

web/src/app/api/products/[id]/route.ts

@@ -0,0 +1,35 @@
+import { NextRequest, NextResponse } from 'next/server';
+
+export async function GET(
+  req: NextRequest,
+  { params }: { params: Promise<{ id: string }> }
+) {
+  const { id } = await params;
+
+  if (!id) {
+    return NextResponse.json(
+      { error: 'product id is required' },
+      { status: 400 }
+    );
+  }
+
+  try {
+    const backendUrl = process.env.BACKEND_URL || 'http://localhost:5000';
+    const url = new URL(`${backendUrl}/api/products/${id}`);
+
+    const res = await fetch(url.toString());
+
+    if (!res.ok) {
+      throw new Error(`Backend returned ${res.status}`);
+    }
+
+    const data = await res.json();
+    return NextResponse.json(data);
+  } catch (error) {
+    console.error('[PRODUCT_DETAIL_ERROR]', error);
+    return NextResponse.json(
+      { error: 'Failed to fetch product details' },
+      { status: 500 }
+    );
+  }
+}

web/src/app/api/products/route.ts

@@ -0,0 +1,40 @@
+import { NextRequest, NextResponse } from 'next/server';
+
+export async function GET(req: NextRequest) {
+  const { searchParams } = new URL(req.url);
+  const type = searchParams.get('type');
+
+  if (!type || !['hotel', 'airline'].includes(type)) {
+    return NextResponse.json(
+      { error: 'type parameter must be "hotel" or "airline"' },
+      { status: 400 }
+    );
+  }
+
+  try {
+    const backendUrl = process.env.BACKEND_URL || 'http://localhost:5000';
+    const url = new URL(`${backendUrl}/api/products/type/${type}`);
+
+    // forward all query params to backend (excluding 'type')
+    searchParams.forEach((value, key) => {
+      if (key !== 'type') {
+        url.searchParams.set(key, value);
+      }
+    });
+
+    const res = await fetch(url.toString());
+
+    if (!res.ok) {
+      throw new Error(`Backend returned ${res.status}`);
+    }
+
+    const data = await res.json();
+    return NextResponse.json(data);
+  } catch (error) {
+    console.error('[PRODUCTS_PROXY_ERROR]', error);
+    return NextResponse.json(
+      { error: 'Failed to fetch products' },
+      { status: 500 }
+    );
+  }
+}

web/src/app/api/session/route.ts

@@ -1,13 +1,12 @@
 import { NextRequest, NextResponse } from 'next/server';
 import { randomUUID } from 'crypto';
-import { getSession, createSession } from '@/lib/sessionStore';
+import { getSession, createSession, setExperiment } from '@/lib/sessionStore';
 
 const COOKIE_NAME = 'phantom_session_id';
 const isProd = process.env.NODE_ENV === 'production';
 
 export async function GET(req: NextRequest) {
     try {
-        // check for existing session cookie
         const existingSession = req.cookies.get(COOKIE_NAME)?.value;
 
         if (existingSession) {
@@ -18,13 +17,11 @@ export async function GET(req: NextRequest) {
             });
         }
 
-        // mint new session id
         const sessionId = randomUUID();
         createSession(sessionId);
 
         const res = NextResponse.json({ sessionId, experimentId: undefined });
 
-        // set httpOnly cookie with security flags
         res.cookies.set({
             name: COOKIE_NAME,
             value: sessionId,
@@ -32,7 +29,7 @@ export async function GET(req: NextRequest) {
             sameSite: 'lax',
             secure: isProd,
             path: '/',
-            maxAge: 60 * 60 * 24 * 30, // 30 days
+            maxAge: 60 * 60 * 24 * 30,
         });
 
         return res;
@@ -44,3 +41,52 @@ export async function GET(req: NextRequest) {
         );
     }
 }
+
+export async function POST(req: NextRequest) {
+    try {
+        const body = await req.json();
+        const { experimentId } = body;
+
+        if (!experimentId) {
+            return NextResponse.json(
+                { error: 'experimentId is required' },
+                { status: 400 }
+            );
+        }
+
+        let sessionId = req.cookies.get(COOKIE_NAME)?.value;
+
+        if (!sessionId) {
+            sessionId = randomUUID();
+            createSession(sessionId);
+        }
+
+        setExperiment(sessionId, experimentId);
+
+        const res = NextResponse.json({
+            sessionId,
+            experimentId,
+            success: true
+        });
+
+        if (!req.cookies.get(COOKIE_NAME)) {
+            res.cookies.set({
+                name: COOKIE_NAME,
+                value: sessionId,
+                httpOnly: true,
+                sameSite: 'lax',
+                secure: isProd,
+                path: '/',
+                maxAge: 60 * 60 * 24 * 30,
+            });
+        }
+
+        return res;
+    } catch (err: any) {
+        console.error('session update error:', err);
+        return NextResponse.json(
+            { error: err.message || 'unknown error' },
+            { status: 500 }
+        );
+    }
+}

web/src/app/cart/page.tsx

@@ -0,0 +1,110 @@
+'use client';
+
+import { Navigation } from '@/components/ui';
+import { useCart } from '@/contexts/CartContext';
+import type { EventName } from '@/lib/events';
+
+const dispatchInteraction = (eventName: EventName, productId?: string, metadata?: Record<string, unknown>) => {
+    const e = new CustomEvent('definedInteraction', {
+        detail: { eventName, productId, metadata },
+    });
+    document.dispatchEvent(e);
+};
+
+export default function CartPage() {
+    const { items, removeItem, clearCart, itemCount } = useCart();
+
+    const handleRemove = (id: string, type: string) => {
+        removeItem(id);
+        dispatchInteraction('remove_item', id, { type });
+    };
+    let itemTypes = Array.from(new Set(items.map(item => item.type)))[0] || 'items';
+
+
+    const total = items.reduce((sum, item) => sum + item.price, 0);
+
+    return (
+        <>
+            <Navigation />
+            <main className="max-w-4xl mx-auto px-4 py-8">
+                <div className="flex justify-between items-center mb-6">
+                    <h1 className="text-3xl font-bold">Shopping Cart</h1>
+                    {itemCount > 0 && (
+                        <button
+                            onClick={clearCart}
+                            className="text-sm text-red-600 hover:underline"
+                        >
+                            Clear cart
+                        </button>
+                    )}
+                </div>
+
+                {itemCount === 0 ? (
+                    <div className="text-center py-12">
+                        <p className="text-gray-500 mb-4">Your cart is empty</p>
+                        <a href="/" className="text-blue-600 hover:underline">Browse our selection</a>
+                    </div>
+                ) : (
+                    <>
+                        <div className="space-y-4 mb-8">
+                            {items.map(item => (
+                                <div
+                                    key={item.id}
+                                    className="flex justify-between items-start p-4 border rounded-lg hover:bg-gray-50"
+                                >
+                                    <div className="flex-1">
+                                        <div className="flex items-center gap-2 mb-1">
+                                            <span className="px-2 py-0.5 text-xs font-medium rounded bg-blue-100 text-blue-800">
+                                                {item.type}
+                                            </span>
+                                            <h3 className="font-semibold">{item.name}</h3>
+                                        </div>
+
+                                        {item.type === 'hotel' && (
+                                            <div className="text-sm text-gray-600">
+                                                <p>{String(item.metadata.roomType)}</p>
+                                                <p>{String(item.metadata.checkIn)} - {String(item.metadata.checkOut)}</p>
+                                                <p>{String(item.metadata.nights)} night{Number(item.metadata.nights) > 1 ? 's' : ''}</p>
+                                            </div>
+                                        )}
+
+                                        {item.type === 'airline' && (
+                                            <div className="text-sm text-gray-600">
+                                                <p>{String(item.metadata.cabinClass)} Class</p>
+                                                <p>{String((item.metadata.departure as any)?.airport)} → {String((item.metadata.arrival as any)?.airport)}</p>
+                                                <p>Duration: {String(item.metadata.duration)}</p>
+                                            </div>
+                                        )}
+                                    </div>
+
+                                    <div className="text-right ml-4">
+                                        <p className="text-xl font-bold mb-2">${item.price}</p>
+                                        <button
+                                            onClick={() => handleRemove(item.id, item.type)}
+                                            className="text-sm text-red-600 hover:underline"
+                                        >
+                                            Remove
+                                        </button>
+                                    </div>
+                                </div>
+                            ))}
+                        </div>
+
+                        <div className="border-t pt-4">
+                            <div className="flex justify-between items-center mb-4">
+                                <span className="text-xl font-semibold">Total</span>
+                                <span className="text-3xl font-bold">${total.toFixed(2)}</span>
+                            </div>
+                            <button
+                                onClick={() => dispatchInteraction('checkout_start', undefined, { total, itemCount })}
+                                className="w-full py-3 bg-blue-600 hover:bg-blue-700 text-white rounded-lg font-medium transition-colors"
+                            >
+                                Proceed to Checkout
+                            </button>
+                        </div>
+                    </>
+                )}
+            </main>
+        </>
+    );
+}

web/src/app/hotel/products/[id]/page.tsx

@@ -0,0 +1,106 @@
+'use client';
+
+import { useState, useEffect } from 'react';
+import { useParams, useRouter } from 'next/navigation';
+import { Navigation } from '@/components/ui';
+import { useCart } from '@/contexts/CartContext';
+import HotelDetails from '@/components/feats/hotel/HotelDetails';
+import { transformProduct, type Hotel, type HotelProduct } from '@/lib/hotel-utils';
+import type { EventName } from '@/lib/events';
+
+const dispatchInteraction = (eventName: EventName, productId?: string, metadata?: Record<string, unknown>) => {
+    const e = new CustomEvent('definedInteraction', {
+        detail: { eventName, productId, metadata },
+    });
+    document.dispatchEvent(e);
+};
+
+export default function HotelProductPage() {
+    const params = useParams();
+    const router = useRouter();
+    const { addItem } = useCart();
+    const [product, setProduct] = useState<Hotel | null>(null);
+    const [loading, setLoading] = useState(true);
+    const [error, setError] = useState<string | null>(null);
+    const [added, setAdded] = useState(false);
+
+    const productId = params.id as string;
+
+    useEffect(() => {
+        const fetchProduct = async () => {
+            try {
+                const res = await fetch(`/api/products/${productId}`);
+                if (!res.ok) throw new Error(`Failed to fetch: ${res.status}`);
+                const json = await res.json();
+                const transformed = transformProduct(json.data as HotelProduct);
+                setProduct(transformed);
+
+                // fire learn_more_about_item event when product loads
+                dispatchInteraction('learn_more_about_item', productId, {
+                    type: 'hotel',
+                    dateIndex: transformed.dateIndex,
+                    roomType: transformed.roomType,
+                });
+            } catch (e) {
+                setError(e instanceof Error ? e.message : 'Failed to load product');
+                console.error('[FETCH_HOTEL_ERROR]', e);
+            } finally {
+                setLoading(false);
+            }
+        };
+        fetchProduct();
+    }, [productId]);
+
+    const handleAddToCart = () => {
+        if (!product) return;
+
+        addItem({
+            id: productId,
+            type: 'hotel',
+            name: product.name,
+            price: product.pricePerNight,
+            metadata: {
+                roomType: product.roomType,
+                nights: product.nights,
+                checkIn: product.checkIn,
+                checkOut: product.checkOut,
+            },
+            dateIndex: product.dateIndex,
+        });
+
+        dispatchInteraction('add_item_to_cart', productId, {
+            type: 'hotel',
+            price: product.pricePerNight,
+        });
+
+        setAdded(true);
+        setTimeout(() => setAdded(false), 2000);
+    };
+
+    return (
+        <>
+            <Navigation />
+            <main className="max-w-4xl mx-auto px-4 py-8">
+                {loading && <div className="text-center py-8">Loading hotel details...</div>}
+                {error && <div className="text-red-500 text-center py-8">{error}</div>}
+
+                {!loading && !error && product && (
+                    <>
+                        <button
+                            onClick={() => router.back()}
+                            className="mt-6 text-blue-600 hover:underline"
+                        >
+                            ← Back to rooms
+                        </button>
+                        <HotelDetails
+                            product={product}
+                            onAddToCart={handleAddToCart}
+                            addedToCart={added}
+                        />
+
+                    </>
+                )}
+            </main>
+        </>
+    );
+}

web/src/app/hotel/products/page.tsx

@@ -1,74 +1,69 @@
 'use client';
 
+import { useState, useEffect, Suspense } from 'react';
+import { useSearchParams } from 'next/navigation';
 import { Navigation } from '@/components/ui';
 import HotelCard from '@/components/feats/hotel/HotelCard';
+import { transformProduct, type Hotel, type HotelProduct } from '@/lib/hotel-utils';
 
-interface Hotel {
-  id: string;
-  name: string;
-  roomType: string;
-  checkIn: string;
-  checkOut: string;
-  amenities: string[];
-  refundable: boolean;
-  pricePerNight: number;
-  nights: number;
+function RoomsList() {
+  const searchParams = useSearchParams();
+  const [rooms, setRooms] = useState<Hotel[]>([]);
+  const [loading, setLoading] = useState(true);
+  const [error, setError] = useState<string | null>(null);
+
+  useEffect(() => {
+    const fetchRooms = async () => {
+      try {
+        const url = new URL('/api/products', window.location.origin);
+        url.searchParams.set('type', 'hotel');
+
+        // forward all relevant search params to the API
+        const params = ['dateIndex', 'destination', 'adults', 'rooms'];
+        params.forEach(param => {
+          const val = searchParams.get(param);
+          if (val) url.searchParams.set(param, val);
+        });
+
+        const res = await fetch(url.toString());
+        if (!res.ok) throw new Error(`Failed to fetch: ${res.status}`);
+        const json = await res.json();
+        const transformed = json.data.map((p: HotelProduct) => transformProduct(p));
+        setRooms(transformed);
+      } catch (e) {
+        setError(e instanceof Error ? e.message : 'Failed to load products');
+        console.error('[FETCH_ERROR]', e);
+      } finally {
+        setLoading(false);
+      }
+    };
+    fetchRooms();
+  }, [searchParams]);
+
+  return (
+    <>
+      <h1 className="text-3xl font-bold mb-6">Available Rooms</h1>
+      {loading && <div className="text-center py-8">Loading...</div>}
+      {error && <div className="text-red-500 text-center py-8">{error}</div>}
+      {!loading && !error && (
+        <div className="space-y-4">
+          {rooms.map((r) => (
+            <HotelCard key={r.id} hotel={r} />
+          ))}
+        </div>
+      )}
+    </>
+  );
 }
 
-const genRandomHotels = (): Hotel[] => {
-  const names = [
-    'Grand Plaza Hotel',
-    'Seaside Resort',
-    'Downtown Suites',
-    'Mountain View Lodge',
-    'City Center Inn',
-    'Luxury Beach Resort',
-    'Urban Boutique Hotel',
-    'Garden View Hotel',
-  ];
-  const roomTypes = ['Standard Room', 'Deluxe Room', 'Suite', 'Executive Suite', 'Premium Room'];
-  const amenities = ['wifi', 'pool', 'gym', 'parking', 'breakfast', 'spa'];
-
-  return Array.from({ length: 10 }, (_, i) => {
-    const nights = Math.floor(Math.random() * 5) + 1;
-    const basePrice = Math.floor(80 + Math.random() * 220);
-    const selectedAmenities = amenities
-      .sort(() => Math.random() - 0.5)
-      .slice(0, Math.floor(Math.random() * 3) + 2);
-
-    const today = new Date();
-    const checkInDate = new Date(today);
-    checkInDate.setDate(today.getDate() + Math.floor(Math.random() * 10));
-    const checkOutDate = new Date(checkInDate);
-    checkOutDate.setDate(checkInDate.getDate() + nights);
-
-    return {
-      id: `htl-${i}`,
-      name: names[i % names.length],
-      roomType: roomTypes[Math.floor(Math.random() * roomTypes.length)],
-      checkIn: checkInDate.toLocaleDateString('en-US', { month: 'short', day: 'numeric' }),
-      checkOut: checkOutDate.toLocaleDateString('en-US', { month: 'short', day: 'numeric' }),
-      amenities: selectedAmenities,
-      refundable: Math.random() > 0.5,
-      pricePerNight: basePrice,
-      nights,
-    };
-  });
-};
-
 export default function HotelProducts() {
-  const hotels = genRandomHotels();
-
   return (
     <>
       <Navigation />
       <main className="max-w-7xl mx-auto px-4 py-8">
-        <h1 className="text-3xl font-bold mb-6">Available Hotels</h1>
-        <div className="space-y-4">
-          {hotels.map((h) => (
-            <HotelCard key={h.id} hotel={h} />
-          ))}
-        </div>
+        <Suspense fallback={<div className="text-center py-8">Loading...</div>}>
+          <RoomsList />
+        </Suspense>
       </main>
     </>
   );

web/src/app/layout.tsx

@@ -2,6 +2,7 @@ import type { Metadata } from "next";
 import { Geist, Geist_Mono } from "next/font/google";
 import "./globals.css";
 import { TrackingProvider } from "@/components/TrackingProvider";
+import { CartProvider } from "@/contexts/CartContext";
 
 const geistSans = Geist({
   variable: "--font-geist-sans",
@@ -28,7 +29,9 @@ export default function RootLayout({
       <body
         className={`${geistSans.variable} ${geistMono.variable} antialiased`}
       >
-        <TrackingProvider>{children}</TrackingProvider>
+        <CartProvider>
+          <TrackingProvider>{children}</TrackingProvider>
+        </CartProvider>
       </body>
     </html>
   );

web/src/app/start-task/page.tsx

@@ -0,0 +1,93 @@
+'use client';
+
+import { useEffect, useState, Suspense } from 'react';
+import { useSearchParams, useRouter } from 'next/navigation';
+
+const StartTaskContent = () => {
+  const searchParams = useSearchParams();
+  const router = useRouter();
+  const [status, setStatus] = useState<'loading' | 'error' | 'redirecting'>('loading');
+  const [error, setError] = useState<string | null>(null);
+
+  useEffect(() => {
+    const uuid = searchParams.get('uuid');
+
+    if (!uuid) {
+      setError('no experiment UUID provided');
+      setStatus('error');
+      return;
+    }
+
+    const validateAndStore = async () => {
+      try {
+        const res = await fetch(`/api/admin/experiments?id=${uuid}`);
+        if (!res.ok) throw new Error('experiment not found');
+
+        const data = await res.json();
+        const exp = data.experiment;
+
+        if (!exp) throw new Error('invalid experiment UUID');
+
+        localStorage.setItem('phantom_experiment_id', uuid);
+
+        await fetch('/api/session', {
+          method: 'POST',
+          headers: { 'Content-Type': 'application/json' },
+          body: JSON.stringify({ experimentId: uuid }),
+        });
+
+        setStatus('redirecting');
+
+        setTimeout(() => {
+          router.push("/");
+        }, 800);
+
+      } catch (err: any) {
+        setError(err.message || 'failed to start task');
+        setStatus('error');
+      }
+    };
+
+    validateAndStore();
+  }, [searchParams, router]);
+
+  return (
+    <div className="flex min-h-screen items-center justify-center bg-zinc-50 dark:bg-black">
+      <div className="text-center">
+        {status === 'loading' && (
+          <div>
+            <div className="mb-4 h-8 w-8 animate-spin rounded-full border-4 border-zinc-200 border-t-zinc-900 dark:border-zinc-800 dark:border-t-zinc-100 mx-auto" />
+            <p className="text-zinc-600 dark:text-zinc-400">validating browser...</p>
+          </div>
+        )}
+
+        {status === 'redirecting' && (
+          <div>
+            <div className="mb-4 text-4xl">✓</div>
+            <p className="text-zinc-900 dark:text-zinc-100 font-medium">website loaded</p>
+            <p className="mt-2 text-sm text-zinc-600 dark:text-zinc-400">redirecting to page...</p>
+          </div>
+        )}
+
+        {status === 'error' && (
+          <div className="rounded-lg bg-red-50 p-6 dark:bg-red-950">
+            <p className="text-red-900 dark:text-red-100 font-medium">error</p>
+            <p className="mt-2 text-sm text-red-700 dark:text-red-300">{error}</p>
+          </div>
+        )}
+      </div>
+    </div>
+  );
+};
+
+export default function StartTaskPage() {
+  return (
+    <Suspense fallback={
+      <div className="flex min-h-screen items-center justify-center bg-zinc-50 dark:bg-black">
+        <p className="text-zinc-600 dark:text-zinc-400">loading...</p>
+      </div>
+    }>
+      <StartTaskContent />
+    </Suspense>
+  );
+}

web/src/components/admin/ExperimentForm.tsx

@@ -0,0 +1,118 @@
+'use client';
+
+import { useState } from 'react';
+
+type ExperimentFormProps = {
+  selectedTaskId?: string;
+  onSuccess?: () => void;
+};
+
+export const ExperimentForm = ({ selectedTaskId, onSuccess }: ExperimentFormProps) => {
+  const [loading, setLoading] = useState(false);
+  const [error, setError] = useState<string | null>(null);
+  const [form, setForm] = useState({
+    subject_name: '',
+    xp_human_only: false,
+    xp_market_mode: 'hotel' as 'hotel' | 'airline',
+  });
+
+  const handleSubmit = async (e: React.FormEvent) => {
+    e.preventDefault();
+    setLoading(true);
+    setError(null);
+
+    try {
+      const res = await fetch('/api/admin/experiments', {
+        method: 'POST',
+        headers: { 'Content-Type': 'application/json' },
+        body: JSON.stringify({
+          ...form,
+          xp_task_id: selectedTaskId || null,
+        }),
+      });
+
+      if (!res.ok) {
+        const data = await res.json();
+        throw new Error(data.error || 'creation failed');
+      }
+
+      setForm({ subject_name: '', xp_human_only: false, xp_market_mode: 'hotel' });
+      onSuccess?.();
+    } catch (err: any) {
+      setError(err.message);
+    } finally {
+      setLoading(false);
+    }
+  };
+
+  return (
+    <form onSubmit={handleSubmit} className="space-y-4 rounded-lg border border-zinc-200 bg-white p-6 dark:border-zinc-800 dark:bg-zinc-950">
+      <h2 className="text-lg font-semibold text-zinc-900 dark:text-zinc-100">
+        Create Experiment
+      </h2>
+
+      {error && (
+        <div className="rounded-lg bg-red-50 p-3 text-sm text-red-800 dark:bg-red-950 dark:text-red-200">
+          {error}
+        </div>
+      )}
+
+      <div>
+        <label className="block text-sm font-medium text-zinc-700 dark:text-zinc-300">
+          subject name
+        </label>
+        <input
+          type="text"
+          value={form.subject_name}
+          onChange={(e) => setForm({ ...form, subject_name: e.target.value })}
+          className="mt-1 w-full rounded-lg border border-zinc-300 bg-white px-3 py-2 text-sm text-zinc-900 focus:border-zinc-900 focus:outline-none dark:border-zinc-700 dark:bg-zinc-900 dark:text-zinc-100 dark:focus:border-zinc-100"
+          placeholder="e.g., baseline_dynamic_pricing_v1"
+          required
+        />
+      </div>
+
+      <div>
+        <label className="block text-sm font-medium text-zinc-700 dark:text-zinc-300">
+          market mode
+        </label>
+        <select
+          value={form.xp_market_mode}
+          onChange={(e) => setForm({ ...form, xp_market_mode: e.target.value as 'hotel' | 'airline' })}
+          className="mt-1 w-full rounded-lg border border-zinc-300 bg-white px-3 py-2 text-sm text-zinc-900 focus:border-zinc-900 focus:outline-none dark:border-zinc-700 dark:bg-zinc-900 dark:text-zinc-100 dark:focus:border-zinc-100"
+        >
+          <option value="hotel">hotel</option>
+          <option value="airline">airline</option>
+        </select>
+      </div>
+
+      <div className="flex items-center gap-2">
+        <input
+          type="checkbox"
+          id="human-only"
+          checked={form.xp_human_only}
+          onChange={(e) => setForm({ ...form, xp_human_only: e.target.checked })}
+          className="h-4 w-4 rounded border-zinc-300 text-zinc-900 focus:ring-zinc-900 dark:border-zinc-700 dark:bg-zinc-900"
+        />
+        <label htmlFor="human-only" className="text-sm text-zinc-700 dark:text-zinc-300">
+          human participants only
+        </label>
+      </div>
+
+      {selectedTaskId && (
+        <div className="rounded-lg bg-zinc-50 p-3 dark:bg-zinc-900">
+          <p className="text-sm text-zinc-600 dark:text-zinc-400">
+            task selected: <span className="font-mono text-xs">{selectedTaskId.slice(0, 8)}...</span>
+          </p>
+        </div>
+      )}
+
+      <button
+        type="submit"
+        disabled={loading}
+        className="w-full rounded-lg bg-black px-4 py-2 text-sm font-medium text-white transition-colors hover:bg-zinc-800 disabled:opacity-50 dark:bg-zinc-50 dark:text-black dark:hover:bg-zinc-200"
+      >
+        {loading ? 'creating experiment...' : 'create experiment'}
+      </button>
+    </form>
+  );
+};

web/src/components/admin/TaskManager.tsx

@@ -0,0 +1,178 @@
+'use client';
+
+import { useState, useEffect } from 'react';
+
+type Task = {
+  id: string;
+  task_name: string;
+  task_description: string;
+  task_def_of_done: string;
+  created_at: string;
+};
+
+type TaskManagerProps = {
+  onTaskSelect?: (taskId: string) => void;
+  selectedTaskId?: string;
+};
+
+export const TaskManager = ({ onTaskSelect, selectedTaskId }: TaskManagerProps) => {
+  const [tasks, setTasks] = useState<Task[]>([]);
+  const [loading, setLoading] = useState(false);
+  const [showForm, setShowForm] = useState(false);
+  const [form, setForm] = useState({
+    task_name: '',
+    task_description: '',
+    task_def_of_done: '',
+  });
+  const [error, setError] = useState<string | null>(null);
+
+  const fetchTasks = async () => {
+    try {
+      const res = await fetch('/api/admin/tasks');
+      if (!res.ok) throw new Error(`fetch failed: ${res.status}`);
+      const data = await res.json();
+      setTasks(data.tasks || []);
+    } catch (err: any) {
+      setError(err.message);
+    }
+  };
+
+  useEffect(() => {
+    fetchTasks();
+  }, []);
+
+  const handleSubmit = async (e: React.FormEvent) => {
+    e.preventDefault();
+    setLoading(true);
+    setError(null);
+
+    try {
+      const res = await fetch('/api/admin/tasks', {
+        method: 'POST',
+        headers: { 'Content-Type': 'application/json' },
+        body: JSON.stringify(form),
+      });
+
+      if (!res.ok) {
+        const data = await res.json();
+        throw new Error(data.error || 'creation failed');
+      }
+
+      setForm({ task_name: '', task_description: '', task_def_of_done: '' });
+      setShowForm(false);
+      await fetchTasks();
+    } catch (err: any) {
+      setError(err.message);
+    } finally {
+      setLoading(false);
+    }
+  };
+
+  return (
+    <div className="space-y-4">
+      <div className="flex items-center justify-between">
+        <h2 className="text-lg font-semibold text-zinc-900 dark:text-zinc-100">
+          Tasks
+        </h2>
+        <button
+          onClick={() => setShowForm(!showForm)}
+          className="rounded-lg bg-zinc-900 px-3 py-1.5 text-sm font-medium text-white transition-colors hover:bg-zinc-700 dark:bg-zinc-100 dark:text-black dark:hover:bg-zinc-300"
+        >
+          {showForm ? 'cancel' : 'new task'}
+        </button>
+      </div>
+
+      {error && (
+        <div className="rounded-lg bg-red-50 p-3 text-sm text-red-800 dark:bg-red-950 dark:text-red-200">
+          {error}
+        </div>
+      )}
+
+      {showForm && (
+        <form onSubmit={handleSubmit} className="space-y-3 rounded-lg border border-zinc-200 bg-white p-4 dark:border-zinc-800 dark:bg-zinc-950">
+          <div>
+            <label className="block text-sm font-medium text-zinc-700 dark:text-zinc-300">
+              task name
+            </label>
+            <input
+              type="text"
+              value={form.task_name}
+              onChange={(e) => setForm({ ...form, task_name: e.target.value })}
+              className="mt-1 w-full rounded-lg border border-zinc-300 bg-white px-3 py-2 text-sm text-zinc-900 focus:border-zinc-900 focus:outline-none dark:border-zinc-700 dark:bg-zinc-900 dark:text-zinc-100 dark:focus:border-zinc-100"
+              placeholder="e.g., Book cheapest flight to Paris"
+              required
+            />
+          </div>
+
+          <div>
+            <label className="block text-sm font-medium text-zinc-700 dark:text-zinc-300">
+              description
+            </label>
+            <textarea
+              value={form.task_description}
+              onChange={(e) => setForm({ ...form, task_description: e.target.value })}
+              className="mt-1 w-full rounded-lg border border-zinc-300 bg-white px-3 py-2 text-sm text-zinc-900 focus:border-zinc-900 focus:outline-none dark:border-zinc-700 dark:bg-zinc-900 dark:text-zinc-100 dark:focus:border-zinc-100"
+              placeholder="User should find and book the cheapest available flight..."
+              rows={3}
+            />
+          </div>
+
+          <div>
+            <label className="block text-sm font-medium text-zinc-700 dark:text-zinc-300">
+              definition of done
+            </label>
+            <textarea
+              value={form.task_def_of_done}
+              onChange={(e) => setForm({ ...form, task_def_of_done: e.target.value })}
+              className="mt-1 w-full rounded-lg border border-zinc-300 bg-white px-3 py-2 text-sm text-zinc-900 focus:border-zinc-900 focus:outline-none dark:border-zinc-700 dark:bg-zinc-900 dark:text-zinc-100 dark:focus:border-zinc-100"
+              placeholder="Booking is completed and confirmation page is shown"
+              rows={2}
+            />
+          </div>
+
+          <button
+            type="submit"
+            disabled={loading}
+            className="w-full rounded-lg bg-black px-4 py-2 text-sm font-medium text-white transition-colors hover:bg-zinc-800 disabled:opacity-50 dark:bg-zinc-50 dark:text-black dark:hover:bg-zinc-200"
+          >
+            {loading ? 'creating...' : 'create task'}
+          </button>
+        </form>
+      )}
+
+      <div className="space-y-2">
+        {tasks.length === 0 ? (
+          <p className="py-8 text-center text-sm text-zinc-500 dark:text-zinc-400">
+            no tasks yet
+          </p>
+        ) : (
+          tasks.map((task) => (
+            <div
+              key={task.id}
+              onClick={() => onTaskSelect?.(task.id)}
+              className={`cursor-pointer rounded-lg border p-3 transition-colors ${
+                selectedTaskId === task.id
+                  ? 'border-zinc-900 bg-zinc-50 dark:border-zinc-100 dark:bg-zinc-900'
+                  : 'border-zinc-200 bg-white hover:border-zinc-300 dark:border-zinc-800 dark:bg-zinc-950 dark:hover:border-zinc-700'
+              }`}
+            >
+              <h3 className="font-medium text-zinc-900 dark:text-zinc-100">
+                {task.task_name}
+              </h3>
+              {task.task_description && (
+                <p className="mt-1 text-sm text-zinc-600 dark:text-zinc-400">
+                  {task.task_description}
+                </p>
+              )}
+              {task.task_def_of_done && (
+                <p className="mt-1 text-xs text-zinc-500 dark:text-zinc-500">
+                  done: {task.task_def_of_done}
+                </p>
+              )}
+            </div>
+          ))
+        )}
+      </div>
+    </div>
+  );
+};

web/src/components/feats/airline/AirlineCard.tsx

@@ -1,6 +1,7 @@
 'use client';
 
 import type { EventName } from '@/lib/events';
+import type { Flight } from '@/lib/airline-utils';
 import { useHoverTracking } from '@/hooks/useHoverTracking';
 import PriceDisplay from '@/components/ui/PriceDisplay';
 
@@ -11,32 +12,17 @@ const dispatchInteraction = (eventName: EventName, productId?: string, metadata?
     document.dispatchEvent(e);
 };
 
-type CabinClass = 'economy' | 'premium' | 'business' | 'first';
-type FareRule = 'flexible' | 'standard' | 'basic';
-
-interface Flight {
-    id: string;
-    departure: { time: string; airport: string };
-    arrival: { time: string; airport: string };
-    duration: string;
-    stops: number;
-    cabinClass: CabinClass;
-    fareRule: FareRule;
-    refundable: boolean;
-    basePrice: number;
-}
-
 export default function AirlineCard({ flight }: { flight: Flight }) {
     const durationRef = useHoverTracking({
         eventName: 'hover_over_title',
         productId: flight.id,
-        metadata: { elementText: flight.duration },
+        metadata: { elementText: flight.duration, dateIndex: flight.dateIndex },
     });
 
     const priceRef = useHoverTracking({
         eventName: 'hover_over_paragraph',
         productId: flight.id,
-        metadata: { elementText: 'price' },
+        metadata: { elementText: 'price', dateIndex: flight.dateIndex },
     });
 
     const handleCardClick = () => {
@@ -44,7 +30,9 @@ export default function AirlineCard({ flight }: { flight: Flight }) {
             cabinClass: flight.cabinClass,
             fareRule: flight.fareRule,
             price: flight.basePrice,
+            dateIndex: flight.dateIndex,
         });
+        window.location.href = `/airline/products/${flight.id}`;
     };
 
     return (

web/src/components/feats/airline/AirlineDetails.tsx

@@ -0,0 +1,75 @@
+'use client';
+
+import type { Flight } from '@/lib/airline-utils';
+
+interface AirlineDetailsProps {
+  product: Flight;
+  onAddToCart: () => void;
+  addedToCart: boolean;
+}
+
+export default function AirlineDetails({ product, onAddToCart, addedToCart }: AirlineDetailsProps) {
+  return (
+    <div className="w-full flex flex-col lg:flex-row gap-12 py-8">
+      {/* Image Section */}
+      <div className="w-full lg:w-1/3 bg-gray-100 rounded-lg aspect-square flex items-center justify-center shrink-0">
+        <span className="text-gray-400 text-lg font-medium">Flight Image</span>
+      </div>
+
+      {/* Details Section */}
+      <div className="flex-1 flex flex-col">
+        <div className="flex justify-between items-start border-b pb-6 mb-6">
+          <div>
+            <h1 className="text-3xl font-bold text-gray-900 mb-1">{product.flightType}</h1>
+            <p className="text-lg text-gray-500">{product.cabinClass} Class</p>
+          </div>
+          <div className="text-right">
+            <p className="text-4xl font-bold text-gray-900">${product.basePrice}</p>
+            {product.refundable && (
+              <span className="inline-block mt-2 px-3 py-1 bg-green-50 text-green-700 rounded-full text-xs font-medium">
+                Refundable
+              </span>
+            )}
+          </div>
+        </div>
+
+        <div className="flex items-center justify-between mb-10">
+          <div className="text-center min-w-[100px]">
+            <p className="text-3xl font-bold text-gray-900">{product.departure.time}</p>
+            <p className="text-sm text-gray-500 font-medium mt-1">{product.departure.airport}</p>
+          </div>
+
+          <div className="flex-1 px-8 flex flex-col items-center">
+            <p className="text-sm text-gray-500 mb-2">{product.duration}</p>
+            <div className="w-full h-0.5 bg-gray-200 relative flex items-center justify-center">
+              <div className="absolute w-3 h-3 bg-gray-400 rounded-full"></div>
+            </div>
+            <p className="text-sm text-gray-500 mt-2">
+              {product.stops === 0 ? 'Nonstop' : `${product.stops} stop${product.stops > 1 ? 's' : ''}`}
+            </p>
+          </div>
+
+          <div className="text-center min-w-[100px]">
+            <p className="text-3xl font-bold text-gray-900">{product.arrival.time}</p>
+            <p className="text-sm text-gray-500 font-medium mt-1">{product.arrival.airport}</p>
+          </div>
+        </div>
+
+        <div className="mt-auto flex items-center justify-between pt-6 border-t">
+          <div className="text-gray-600">
+            <span className="font-bold text-gray-900">{product.availability}</span> seats remaining
+            <span className="mx-2">•</span>
+            {product.fareRule}
+          </div>
+          <button
+            onClick={onAddToCart}
+            disabled={addedToCart}
+            className="px-8 py-4 bg-black hover:bg-gray-800 disabled:bg-green-600 text-white rounded-lg text-lg font-medium transition-all min-w-[200px]"
+          >
+            {addedToCart ? 'In Cart' : 'Add to Cart'}
+          </button>
+        </div>
+      </div>
+    </div>
+  );
+}

web/src/components/feats/airline/AirlineHero.tsx

@@ -1,7 +1,9 @@
 'use client';
 
 import { useState, FormEvent } from 'react';
+import { useRouter } from 'next/navigation';
 import { Button, Label, Input, DateInput, RadioGroup, Dropdown, DropdownCounter } from '@/components/ui';
+import { dateToDaysFromToday } from '@/lib/airline-utils';
 
 type TripType = 'roundtrip' | 'oneway' | 'multicity';
 
@@ -19,6 +21,7 @@ const LocationIcon = () => (
 );
 
 export default function AirlineHero() {
+  const router = useRouter();
   const [tripType, setTripType] = useState<TripType>('roundtrip');
   const [origin, setOrigin] = useState('');
   const [destination, setDestination] = useState('');
@@ -28,7 +31,23 @@ export default function AirlineHero() {
 
   const handleSearch = (e: FormEvent) => {
     e.preventDefault();
-    console.log({ tripType, origin, destination, departDate, returnDate, passengers });
+    const params = new URLSearchParams();
+
+    if (departDate) {
+      const daysOffset = dateToDaysFromToday(departDate);
+      params.set('dateIndex', daysOffset.toString());
+    }
+
+    if (origin) params.set('origin', origin);
+    if (destination) params.set('destination', destination);
+    if (tripType !== 'roundtrip') params.set('tripType', tripType);
+    if (returnDate && tripType === 'roundtrip') params.set('returnDate', returnDate);
+
+    params.set('adults', passengers.adults.toString());
+    params.set('children', passengers.children.toString());
+    params.set('infants', passengers.infants.toString());
+
+    router.push(`/airline/products?${params.toString()}`);
   };
 
   const totalPax = passengers.adults + passengers.children + passengers.infants;

web/src/components/feats/hotel/HotelCard.tsx

@@ -1,6 +1,7 @@
 'use client';
 
 import type { EventName } from '@/lib/events';
+import type { Hotel } from '@/lib/hotel-utils';
 import { useHoverTracking } from '@/hooks/useHoverTracking';
 import PriceDisplay from '@/components/ui/PriceDisplay';
 
@@ -11,18 +12,6 @@ const dispatchInteraction = (eventName: EventName, productId?: string, metadata?
     document.dispatchEvent(e);
 };
 
-interface Hotel {
-    id: string;
-    name: string;
-    roomType: string;
-    checkIn: string;
-    checkOut: string;
-    amenities: string[];
-    refundable: boolean;
-    pricePerNight: number;
-    nights: number;
-}
-
 const AmenityIcon = ({ name }: { name: string }) => {
     const iconMap: Record<string, string> = {
         wifi: 'Wi-Fi',
@@ -39,13 +28,13 @@ export default function HotelCard({ hotel }: { hotel: Hotel }) {
     const titleRef = useHoverTracking({
         eventName: 'hover_over_title',
         productId: hotel.id,
-        metadata: { elementText: hotel.name },
+        metadata: { elementText: hotel.name, dateIndex: hotel.dateIndex },
     });
 
     const priceRef = useHoverTracking({
         eventName: 'hover_over_paragraph',
         productId: hotel.id,
-        metadata: { elementText: 'price' },
+        metadata: { elementText: 'price', dateIndex: hotel.dateIndex },
     });
 
     const handleCardClick = () => {
@@ -53,7 +42,9 @@ export default function HotelCard({ hotel }: { hotel: Hotel }) {
             roomType: hotel.roomType,
             price: hotel.pricePerNight,
             nights: hotel.nights,
+            dateIndex: hotel.dateIndex,
         });
+        window.location.href = `/hotel/products/${hotel.id}`;
     };
 
     return (

web/src/components/feats/hotel/HotelDetails.tsx

@@ -0,0 +1,74 @@
+'use client';
+
+import type { Hotel } from '@/lib/hotel-utils';
+
+interface HotelDetailsProps {
+  product: Hotel;
+  onAddToCart: () => void;
+  addedToCart: boolean;
+}
+
+export default function HotelDetails({ product, onAddToCart, addedToCart }: HotelDetailsProps) {
+  return (
+    <div className="w-full flex flex-col lg:flex-row gap-12 py-8">
+      {/* Image Section - Larger and cleaner */}
+      <div className="w-full lg:w-1/2 bg-gray-100 rounded-lg aspect-[4/3] flex items-center justify-center shrink-0">
+        <span className="text-gray-400 text-lg font-medium">Hotel Image</span>
+      </div>
+
+      {/* Details Section - Full height/width usage */}
+      <div className="flex-1 flex flex-col">
+        <div className="border-b pb-6 mb-6">
+          <h1 className="text-4xl font-bold text-gray-900 mb-2">{product.name}</h1>
+          <p className="text-xl text-gray-500">{product.roomType}</p>
+        </div>
+
+        <div className="grid grid-cols-2 gap-8 mb-8">
+          <div>
+            <h3 className="text-sm font-semibold text-gray-900 uppercase tracking-wider mb-2">Check-in</h3>
+            <p className="text-lg text-gray-700">{product.checkIn}</p>
+          </div>
+          <div>
+            <h3 className="text-sm font-semibold text-gray-900 uppercase tracking-wider mb-2">Check-out</h3>
+            <p className="text-lg text-gray-700">{product.checkOut}</p>
+          </div>
+        </div>
+
+        <div className="mb-8">
+          <h3 className="text-sm font-semibold text-gray-900 uppercase tracking-wider mb-3">Amenities</h3>
+          <div className="flex flex-wrap gap-3">
+            {product.amenities.map(a => (
+              <span key={a} className="px-3 py-1.5 bg-gray-100 text-gray-700 rounded-md text-sm font-medium">
+                {a}
+              </span>
+            ))}
+          </div>
+        </div>
+
+        {product.refundable && (
+          <div className="mb-8 p-4 bg-green-50 text-green-800 rounded-md inline-block">
+            <span className="font-medium">Free cancellation available</span>
+          </div>
+        )}
+
+        <div className="mt-auto pt-6 border-t flex items-center justify-between">
+          <div>
+            <p className="text-sm text-gray-500 mb-1">Total for {product.nights} night{product.nights > 1 ? 's' : ''}</p>
+            <div className="flex items-baseline gap-2">
+              <span className="text-4xl font-bold text-gray-900">${product.pricePerNight * product.nights}</span>
+              <span className="text-gray-500">/ {product.nights} nights</span>
+            </div>
+          </div>
+
+          <button
+            onClick={onAddToCart}
+            disabled={addedToCart}
+            className="px-8 py-4 bg-black hover:bg-gray-800 disabled:bg-green-600 text-white rounded-lg text-lg font-medium transition-all min-w-[200px]"
+          >
+            {addedToCart ? 'In Cart' : 'Add to Cart'}
+          </button>
+        </div>
+      </div>
+    </div>
+  );
+}

web/src/components/feats/hotel/HotelHero.tsx

@@ -1,7 +1,9 @@
 'use client';
 
 import { useState, FormEvent } from 'react';
+import { useRouter } from 'next/navigation';
 import { Button, Label, Input, DateInput, Dropdown, DropdownCounter } from '@/components/ui';
+import { dateToDaysFromToday } from '@/lib/hotel-utils';
 
 const LocationIcon = () => (
   <svg className="w-5 h-5" fill="none" stroke="currentColor" viewBox="0 0 24 24">
@@ -11,14 +13,25 @@ const LocationIcon = () => (
 );
 
 export default function HotelHero() {
+  const router = useRouter();
   const [destination, setDestination] = useState('');
   const [checkIn, setCheckIn] = useState('');
-  const [checkOut, setCheckOut] = useState('');
   const [guests, setGuests] = useState({ adults: 2, rooms: 1 });
 
   const handleSearch = (e: FormEvent) => {
     e.preventDefault();
-    console.log({ destination, checkIn, checkOut, guests });
+    const params = new URLSearchParams();
+
+    if (checkIn) {
+      const daysOffset = dateToDaysFromToday(checkIn);
+      params.set('dateIndex', daysOffset.toString());
+    }
+
+    if (destination) params.set('destination', destination);
+    params.set('adults', guests.adults.toString());
+    params.set('rooms', guests.rooms.toString());
+
+    router.push(`/hotel/products?${params.toString()}`);
   };
 
   return (
@@ -26,16 +39,16 @@ export default function HotelHero() {
       <div className="w-full max-w-4xl px-4">
         <div className="text-center mb-8">
           <h1 className="text-4xl md:text-5xl font-bold mb-4">
-            Find your perfect stay
+            Find your perfect room
           </h1>
           <p className="text-lg">
-            Search hotels, compare prices, and book with confidence
+            Search rooms, compare prices, and book with confidence
           </p>
         </div>
 
         <form onSubmit={handleSearch} className="search-form">
-          <div className="grid grid-cols-1 sm:grid-cols-2 lg:grid-cols-4 gap-4">
-            <div className="sm:col-span-2">
+          <div className="grid grid-cols-1 sm:grid-cols-2 lg:grid-cols-3 gap-4">
+            <div>
               <Label htmlFor="destination">Where to?</Label>
               <Input
                 type="text"
@@ -49,7 +62,7 @@ export default function HotelHero() {
             </div>
 
             <div>
-              <Label htmlFor="checkIn">Check-in</Label>
+              <Label htmlFor="checkIn">Date (1 night stay)</Label>
               <DateInput
                 id="checkIn"
                 value={checkIn}
@@ -59,43 +72,27 @@ export default function HotelHero() {
             </div>
 
             <div>
-              <Label htmlFor="checkOut">Check-out</Label>
-              <DateInput
-                id="checkOut"
-                value={checkOut}
-                onChange={(e) => setCheckOut(e.target.value)}
-                required
-              />
-            </div>
-
-            <div className="sm:col-span-2 lg:col-span-4">
-              <Label htmlFor="guests">Guests & Rooms</Label>
-              <Dropdown label={`${guests.adults} ${guests.adults === 1 ? 'adult' : 'adults'}, ${guests.rooms} ${guests.rooms === 1 ? 'room' : 'rooms'}`}>
+              <Label htmlFor="guests">Guests</Label>
+              <Dropdown label={`${guests.adults} ${guests.adults === 1 ? 'adult' : 'adults'}`}>
                 <DropdownCounter
                   label="Adults"
                   value={guests.adults}
                   min={1}
                   onChange={(v) => setGuests({ ...guests, adults: v })}
                 />
-                <DropdownCounter
-                  label="Rooms"
-                  value={guests.rooms}
-                  min={1}
-                  onChange={(v) => setGuests({ ...guests, rooms: v })}
-                />
               </Dropdown>
             </div>
 
-            <div className="sm:col-span-2 lg:col-span-4">
+            <div className="sm:col-span-2 lg:col-span-3">
               <Button type="submit" fullWidth>
-                Search Hotels
+                Search Rooms
               </Button>
             </div>
           </div>
         </form>
 
         <div className="mt-6 text-center text-sm">
-          <p>Over 2 million hotels worldwide · Best price guarantee · Free cancellation on most bookings</p>
+          <p>Over 2 million rooms worldwide · Best price guarantee · Free cancellation on most bookings</p>
         </div>
       </div>
     </div>

web/src/contexts/CartContext.tsx

@@ -0,0 +1,76 @@
+'use client';
+
+import { createContext, useContext, useState, useEffect, ReactNode } from 'react';
+
+export interface CartItem {
+  id: string;
+  type: 'hotel' | 'airline';
+  name: string;
+  price: number;
+  metadata: Record<string, unknown>;
+  dateIndex: number;
+}
+
+interface CartContextType {
+  items: CartItem[];
+  addItem: (item: CartItem) => void;
+  removeItem: (id: string) => void;
+  clearCart: () => void;
+  itemCount: number;
+}
+
+const CartContext = createContext<CartContextType | undefined>(undefined);
+
+const CART_KEY = 'phantom_cart';
+
+export const CartProvider = ({ children }: { children: ReactNode }) => {
+  const [items, setItems] = useState<CartItem[]>([]);
+  const [loaded, setLoaded] = useState(false);
+
+  // load cart from sessionStorage on mount
+  useEffect(() => {
+    const stored = sessionStorage.getItem(CART_KEY);
+    if (stored) {
+      try {
+        setItems(JSON.parse(stored));
+      } catch (e) {
+        console.error('[CART_LOAD]', e);
+      }
+    }
+    setLoaded(true);
+  }, []);
+
+  // persist to sessionStorage whenever cart changes
+  useEffect(() => {
+    if (!loaded) return;
+    sessionStorage.setItem(CART_KEY, JSON.stringify(items));
+  }, [items, loaded]);
+
+  const addItem = (item: CartItem) => {
+    setItems(prev => {
+      // prevent duplicates
+      if (prev.find(i => i.id === item.id)) return prev;
+      return [...prev, item];
+    });
+  };
+
+  const removeItem = (id: string) => {
+    setItems(prev => prev.filter(i => i.id !== id));
+  };
+
+  const clearCart = () => {
+    setItems([]);
+  };
+
+  return (
+    <CartContext.Provider value={{ items, addItem, removeItem, clearCart, itemCount: items.length }}>
+      {children}
+    </CartContext.Provider>
+  );
+};
+
+export const useCart = () => {
+  const ctx = useContext(CartContext);
+  if (!ctx) throw new Error('useCart must be used within CartProvider');
+  return ctx;
+};

web/src/hooks/useInteractionTracking.ts

@@ -1,5 +1,5 @@
 import { useEffect, useRef, useState } from 'react';
-import '@/lib/experiments' // ensure experiments lib is loaded
+import '@/lib/experiments'
 import type { EventName } from '@/lib/events';
 
 const fetchSessionId = async (): Promise<string> => {
@@ -21,10 +21,14 @@ const track = async (ev: {
   metadata?: Record<string, unknown>;
 }) => {
   try {
+    const experimentId = localStorage.getItem('phantom_experiment_id');
     await fetch('/api/ingest', {
       method: 'POST',
       headers: { 'Content-Type': 'application/json' },
-      body: JSON.stringify(ev),
+      body: JSON.stringify({
+        ...ev,
+        experimentId: experimentId || undefined,
+      }),
     });
   } catch (err) {
     console.error('track failed:', err);

web/src/lib/airline-utils.ts

@@ -0,0 +1,75 @@
+export interface AirlineProduct {
+  id: string;
+  flight_type: string;
+  date_index: number;
+  metadata: {
+    departure: { time: string; airport: string };
+    arrival: { time: string; airport: string };
+    duration: string;
+    stops: number;
+    cabin_class: string;
+    fare_rule: string;
+    refundable: boolean;
+    total?: number;
+    base_price: number;
+  };
+  availability: number;
+}
+
+export interface Flight {
+  id: string;
+  flightType: string;
+  departure: { time: string; airport: string };
+  arrival: { time: string; airport: string };
+  duration: string;
+  stops: number;
+  cabinClass: string;
+  fareRule: string;
+  refundable: boolean;
+  basePrice: number;
+  dateIndex: number;
+  availability: number;
+}
+
+const EPOCH = new Date(0);
+
+export const transformProduct = (p: AirlineProduct): Flight => {
+  const { id, flight_type, date_index, metadata, availability } = p;
+
+  return {
+    id,
+    flightType: flight_type,
+    departure: metadata.departure,
+    arrival: metadata.arrival,
+    duration: metadata.duration,
+    stops: metadata.stops,
+    cabinClass: metadata.cabin_class,
+    fareRule: metadata.fare_rule,
+    refundable: metadata.refundable,
+    basePrice: metadata.base_price,
+    dateIndex: date_index,
+    availability,
+  };
+};
+
+// convert date string to days from today
+export const dateToDaysFromToday = (dateStr: string): number => {
+  const target = new Date(dateStr);
+  target.setHours(0, 0, 0, 0);
+  const today = new Date();
+  today.setHours(0, 0, 0, 0);
+  return Math.floor((target.getTime() - today.getTime()) / 86400000);
+};
+
+// convert date string to date_index (days since epoch)
+export const dateToIndex = (dateStr: string): number => {
+  const d = new Date(dateStr);
+  return Math.floor((d.getTime() - EPOCH.getTime()) / 86400000);
+};
+
+// get current date_index
+export const todayIndex = (): number => {
+  const now = new Date();
+  now.setHours(0, 0, 0, 0);
+  return Math.floor((now.getTime() - EPOCH.getTime()) / 86400000);
+};

web/src/lib/hotel-utils.ts

@@ -0,0 +1,71 @@
+export interface HotelProduct {
+  id: string;
+  room_type: string;
+  date_index: number;
+  metadata: {
+    amenities?: string[];
+    total?: number;
+    image_url?: string;
+    base_price?: number;
+    name?: string;
+    refundable?: boolean;
+  };
+  availability: number;
+}
+
+export interface Hotel {
+  id: string;
+  name: string;
+  roomType: string;
+  checkIn: string;
+  checkOut: string;
+  dateIndex: number;
+  amenities: string[];
+  refundable: boolean;
+  pricePerNight: number;
+  nights: number;
+}
+
+const EPOCH = new Date(0);
+
+export const transformProduct = (p: HotelProduct): Hotel => {
+  const { id, room_type, date_index, metadata } = p;
+  const checkIn = new Date(EPOCH.getTime() + date_index * 86400000);
+  const nights = 1;
+  const checkOut = new Date(checkIn.getTime() + nights * 86400000);
+
+  return {
+    id,
+    name: metadata?.name || room_type,
+    roomType: room_type,
+    checkIn: checkIn.toLocaleDateString('en-US', { month: 'short', day: 'numeric' }),
+    checkOut: checkOut.toLocaleDateString('en-US', { month: 'short', day: 'numeric' }),
+    dateIndex: date_index,
+    amenities: metadata?.amenities || [],
+    refundable: metadata?.refundable || false,
+    pricePerNight: metadata?.base_price || 100,
+    nights,
+  };
+};
+
+// convert date string to days from today
+export const dateToDaysFromToday = (dateStr: string): number => {
+  const target = new Date(dateStr);
+  target.setHours(0, 0, 0, 0);
+  const today = new Date();
+  today.setHours(0, 0, 0, 0);
+  return Math.floor((target.getTime() - today.getTime()) / 86400000);
+};
+
+// convert date string to date_index (days since epoch)
+export const dateToIndex = (dateStr: string): number => {
+  const d = new Date(dateStr);
+  return Math.floor((d.getTime() - EPOCH.getTime()) / 86400000);
+};
+
+// get current date_index
+export const todayIndex = (): number => {
+  const now = new Date();
+  now.setHours(0, 0, 0, 0);
+  return Math.floor((now.getTime() - EPOCH.getTime()) / 86400000);
+};

web/src/lib/product-utils.ts

@@ -0,0 +1,25 @@
+import { HotelProduct, Hotel, transformProduct as transformHotel } from './hotel-utils';
+import { AirlineProduct, Flight, transformProduct as transformFlight } from './airline-utils';
+
+export type Product = Hotel | Flight;
+export type ProductRaw = HotelProduct | AirlineProduct;
+
+export const isHotelProduct = (p: ProductRaw): p is HotelProduct => {
+  return 'room_type' in p;
+};
+
+export const isAirlineProduct = (p: ProductRaw): p is AirlineProduct => {
+  return 'flight_type' in p;
+};
+
+export const transformProduct = (p: ProductRaw): Product => {
+  if (isHotelProduct(p)) {
+    return transformHotel(p);
+  }
+  return transformFlight(p);
+};
+
+export const getProductType = (p: Product): 'hotel' | 'airline' => {
+  if ('roomType' in p) return 'hotel';
+  return 'airline';
+};

web/src/proxy.ts

@@ -10,6 +10,8 @@ export function proxy(req: NextRequest) {
         pathname.startsWith('/admin') ||
         pathname.startsWith('/_next') ||
         pathname.startsWith('/static') ||
+        pathname.startsWith('/start-task') ||
+        pathname.startsWith('/cart') ||
         pathname.includes('.')
         // TODO: add robots.txt and sitemap.xml if needed here
     ) {

web/src/utils/supabase/client.ts

@@ -0,0 +1,10 @@
+import { createBrowserClient } from "@supabase/ssr";
+
+const supabaseUrl = process.env.NEXT_PUBLIC_SUPABASE_URL;
+const supabaseKey = process.env.NEXT_PUBLIC_SUPABASE_ANON_KEY;
+
+export const createClient = () =>
+    createBrowserClient(
+        supabaseUrl!,
+        supabaseKey!,
+    );

web/src/utils/supabase/middleware.ts

@@ -0,0 +1,37 @@
+import { createServerClient, type CookieOptions } from "@supabase/ssr";
+import { type NextRequest, NextResponse } from "next/server";
+
+const supabaseUrl = process.env.NEXT_PUBLIC_SUPABASE_URL;
+const supabaseKey = process.env.NEXT_PUBLIC_SUPABASE_ANON_KEY;
+
+export const createClient = (request: NextRequest) => {
+  // Create an unmodified response
+  let supabaseResponse = NextResponse.next({
+    request: {
+      headers: request.headers,
+    },
+  });
+
+  const supabase = createServerClient(
+    supabaseUrl!,
+    supabaseKey!,
+    {
+      cookies: {
+        getAll() {
+          return request.cookies.getAll()
+        },
+        setAll(cookiesToSet) {
+          cookiesToSet.forEach(({ name, value, options }) => request.cookies.set(name, value))
+          supabaseResponse = NextResponse.next({
+            request,
+          })
+          cookiesToSet.forEach(({ name, value, options }) =>
+            supabaseResponse.cookies.set(name, value, options)
+          )
+        },
+      },
+    },
+  );
+
+  return supabaseResponse
+};

web/src/utils/supabase/server.ts

@@ -0,0 +1,27 @@
+import { createServerClient, type CookieOptions } from "@supabase/ssr";
+import { cookies } from "next/headers";
+import { ReadonlyRequestCookies } from "next/dist/server/web/spec-extension/adapters/request-cookies";
+
+const supabaseUrl = process.env.NEXT_PUBLIC_SUPABASE_URL;
+const supabaseKey = process.env.NEXT_PUBLIC_SUPABASE_ANON_KEY;
+
+export const createClient = (cookieStore: ReadonlyRequestCookies) => {
+    return createServerClient(
+        supabaseUrl!,
+        supabaseKey!,
+        {
+            cookies: {
+                getAll() {
+                    return cookieStore.getAll()
+                },
+                setAll(cookiesToSet) {
+                    try {
+                        cookiesToSet.forEach(({ name, value, options }) => cookieStore.set(name, value, options))
+                    } catch {
+                        // `setAll` called from Server Component - ignored if middleware handles session refresh
+                    }
+                },
+            },
+        },
+    );
+};