feature: e2e pricing pipeline with inference

* supabase product proxy and rendering

* minor pipeline refactor

* refactoring and demand estimation

* trackion of date index searching

* fixing changes of imports

* data seeding

* chore: airline basic refactor

* feat: huge push of product changes and item review with cart

* refactored design

* chore: moving route elsewhere and align

* fix: build of web/

* chore: fixing paper build

* fixing chars

.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 +1,8 @@
 **/.env
 **/.venv
-PHANTOM.wiki/
+**/__pycache__
-**/.virtual_documents/
-**/__pycache__/
 **/.ipynb_checkpoints/
+**/.virtual_documents/
+**/session_*.svg
+**/*graph.svg
+paper/src/bib/auto

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 +39,14 @@ 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
+
+.PHONY: all pdf clean watch run.webapp install test

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():
+def dump_logs(
-    # TODO: implement a dump of logs of time period t_start to t_end (params of get)
+    topic: str = 'user-interactions',
-    # OR: allow for params of last_n logs as a param - creating two modes of the dumping
+    last_n: Optional[int] = None,
-    pass
+    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

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/procesing/__init__.py

@@ -0,0 +1,19 @@
+from .extract import (
+    KafkaDataFetcher,
+    ExperimentJoiner,
+    EventTitleAugmenter,
+)
+from .demand import DemandEstimator
+from .mapping import SessionTransitionProbMatrixTransformer, render_graph
+from .pipeline import etl_pipeline, pricing_pipeline
+
+__all__ = [
+    'KafkaDataFetcher',
+    'ExperimentJoiner',
+    'EventTitleAugmenter',
+    'DemandEstimator',
+    'SessionTransitionProbMatrixTransformer',
+    'render_graph',
+    'etl_pipeline',
+    'pricing_pipeline',
+]

experiments/procesing/demand.py

@@ -0,0 +1,119 @@
+from sklearn.base import BaseEstimator, TransformerMixin
+import numpy as np
+import pandas as pd
+from supabase import create_client, Client
+from typing import Optional, Literal
+import os
+import logging
+log = logging.getLogger(__name__)
+
+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 ChunkInteractionsIntoSteps(BaseEstimator, TransformerMixin):
+    """
+    Split interaction data into time windows for temporal analysis.
+    Returns a list of dataframes, one per time window.
+    """
+    def __init__(self,
+                 window_size:str='1h',
+                 ts_col:str='ts',
+                 return_metadata:bool=True):
+        """
+        Args:
+            window_size: pandas freq string ('1h', '30T', '1D', etc)
+            ts_col: timestamp column name
+            return_metadata: if True, return dict with metadata per chunk
+        """
+        self.window_size = window_size
+        self.ts_col = ts_col
+        self.return_metadata = return_metadata
+
+    def fit(self, X):
+        return self
+
+    def transform(self, interactions: pd.DataFrame):
+        """
+        Returns:
+            if return_metadata=False: list of dataframes, one per window
+            if return_metadata=True: list of dicts with keys:
+                - 'data': dataframe for this window
+                - 'window_start': start timestamp
+                - 'window_end': end timestamp
+                - 'window_idx': integer index
+        """
+        if interactions.empty:
+            return []
+
+        df = interactions.copy()
+
+        # ensure timestamp is datetime
+        if not pd.api.types.is_datetime64_any_dtype(df[self.ts_col]):
+            df[self.ts_col] = pd.to_datetime(df[self.ts_col])
+
+        # sort by time
+        df = df.sort_values(self.ts_col)
+
+        # assign window
+        df['_window'] = df[self.ts_col].dt.floor(self.window_size)
+
+        # group by window
+        chunks = []
+        for idx, (window_start, group) in enumerate(df.groupby('_window')):
+            chunk_data = group.drop(columns=['_window'])
+
+            if self.return_metadata:
+                chunks.append({
+                    'data': chunk_data,
+                    'window_start': window_start,
+                    'window_end': window_start + pd.Timedelta(self.window_size),
+                    'window_idx': idx
+                })
+            else:
+                chunks.append(chunk_data)
+
+        return chunks
+
+
+class DemandEstimator(BaseEstimator, TransformerMixin):
+    def __init__(self,
+                 store_mode:str='hotel',
+                 session_filter:str="",
+                 experiment_filter:str=""):
+        self.store=store_mode
+        self.session_filter=session_filter if len(session_filter)>0 else None
+        self.experiment_filter=experiment_filter if len(experiment_filter)>0 else None
+    def fit(self, X):
+        return self
+
+    def transform(self, interactions : pd.DataFrame):
+        if interactions.empty:
+            return pd.DataFrame(columns=["productId", "demand_score"])
+        if self.session_filter:
+            interactions = interactions[interactions['sessionId'] == self.session_filter]
+        if self.experiment_filter:
+            interactions = interactions[interactions['experimentId'] == self.experiment_filter]
+
+        products=supabase.table(f'{self.store}_products').select("id, room_type, date_index, metadata, availability").execute()
+        products = pd.DataFrame(products.data)
+        unique_products = products['id'].unique()
+        log.info(f"Demand estimator found {len(unique_products)} in data")
+
+        # filter out rows without productId
+        interactions_with_products = interactions.dropna(subset=['productId'])
+
+        if interactions_with_products.empty:
+            # no interactions with products, return all zeros
+            return pd.DataFrame({
+                'productId': unique_products,
+                'demand_score': 0
+            })
+
+        # TODO: improve demand score calculation rather than just counting interactions (use weights..)
+        # while maintaining simplicity of a simple cross tab approach
+        product_demand = pd.crosstab(interactions_with_products['productId'], "no_of_interactions")
+        product_demand = product_demand.reindex(unique_products, fill_value=0).reset_index()
+        product_demand.columns = ['productId', 'demand_score']
+        return product_demand

experiments/procesing/elasticity.py

@@ -0,0 +1,333 @@
+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."""
+        series_by_product = {}
+
+        for chunk in aligned_chunks:
+            demand_df = chunk['demand']
+            price_df = chunk['prices']
+
+            # merge on productId
+            merged = demand_df.merge(price_df, 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 _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/extract.py

@@ -0,0 +1,207 @@
+import pandas as pd
+import json
+import numpy as np
+import os
+import requests
+from dotenv import load_dotenv
+from sklearn.base import BaseEstimator, TransformerMixin
+from supabase import create_client, Client
+from typing import Tuple, List, Dict
+load_dotenv()
+
+BACKEND_URL = os.getenv("BACKEND_URL", "http://localhost:5000")
+SUPABASE_URL = os.getenv("NEXT_PUBLIC_SUPABASE_URL")
+SUPABASE_KEY = os.getenv("NEXT_PUBLIC_SUPABASE_ANON_KEY")
+N_PRICE_BUCKETS = 5
+
+supabase: Client = create_client(SUPABASE_URL, SUPABASE_KEY)
+
+
+class KafkaDataFetcher(BaseEstimator, TransformerMixin):
+    def __init__(self, topic: str = "user-interactions"):
+        self.topic = topic # also can be price-logs
+    def fit(self, X=None, y=None):
+        return self
+
+    def transform(self, X=None):
+        resp = requests.get(f"{BACKEND_URL}/api/kafka/dump?topic={self.topic}")
+        resp.raise_for_status()
+        data = resp.json()
+
+        if not data.get('success') or not data.get('data'):
+            return pd.DataFrame()
+
+        df = pd.DataFrame(data['data'])
+        if self.topic == 'user-interactions':
+            if 'metadata' in df.columns: # explode metadata col json
+                df = df.join(pd.json_normalize(df.pop("metadata"), sep=".").add_prefix("metadata_"))
+            df = df.dropna(subset=['eventName'])
+            # remape dateIndex
+            df['dateIndex'] = df['metadata_dateIndex'].astype('Int64')
+        return df
+
+
+class ExperimentJoiner(BaseEstimator, TransformerMixin):
+    def fit(self, X=None, y=None):
+        return self
+
+    def transform(self, df):
+        if df.empty or 'experimentId' not in df.columns:
+            return df
+
+        unique_exp_ids = df['experimentId'].dropna().unique()
+        if len(unique_exp_ids) == 0:
+            return df
+
+        resp = 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', unique_exp_ids.tolist()).execute()
+
+        if not resp.data:
+            return df
+
+        exp_df = pd.DataFrame(resp.data)
+
+        # flatten task nested object if present
+        if 'task' in exp_df.columns and exp_df['task'].notnull().any():
+            task_normalized = pd.json_normalize(exp_df['task'].dropna())
+            task_normalized.index = exp_df[exp_df['task'].notnull()].index
+            exp_df = exp_df.drop(columns=['task']).join(task_normalized, rsuffix='_task')
+
+        # rename experiment columns for clarity
+        exp_df = exp_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'
+        })
+
+        df = df.merge(exp_df, on='experimentId', how='left')
+        return df
+
+
+class EventTitleAugmenter(BaseEstimator, TransformerMixin):
+    def fit(self, X=None, y=None):
+        return self
+
+    def transform(self, df):
+        # from taking standard view_item_page in eventName to view_item_page_{metadata_schema}
+        # we want metadata schema to create product specific event names
+
+        # only create price buckets if we have enough unique prices
+        if df["metadata_price"].notnull().sum() > 0:
+            try:
+                price_buckets = pd.qcut(
+                    df["metadata_price"],
+                    q=N_PRICE_BUCKETS,
+                    labels=[f"PB_{i+1}" for i in range(N_PRICE_BUCKETS)],
+                    duplicates='drop'  # handle duplicate bin edges
+                )
+            except ValueError:
+                # fallback: if still not enough unique values, use cut with fixed ranges or just use raw price
+                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)
+
+        # metadata_schema: _product_id@price_bucket_{i} only if we have product metadata otherswise keep original event name
+        # TODO: make this adaptive, if we have hover_over_title we append the title, if its view_page we say which page
+        df["metadata_schema"] = np.where(
+            df["productId"].notnull() & df["metadata_price"].notnull(),
+            "_" + df["productId"].astype(str) + "@" + price_buckets.astype(str),
+            ""
+        )
+        df["eventName"] = df["eventName"] + df["metadata_schema"].astype(str)
+        return df
+
+
+def chunk_shared_data(interactions_df: pd.DataFrame,
+                      price_logs_df: pd.DataFrame,
+                      window_size: str = '30s',
+                      ts_col: str = 'ts') -> Tuple[List[Dict], List[Dict]]:
+    """
+    Chunk interaction and price data into aligned time windows.
+
+    Args:
+        interactions_df: interaction data with timestamp column
+        price_logs_df: price log data with timestamp column
+        window_size: pandas freq string ('30s', '1min', '1h', etc)
+        ts_col: name of timestamp column
+
+    Returns:
+        tuple of (interaction_chunks, price_chunks) where each is list of dicts:
+        {
+            'window_start': timestamp,
+            'window_end': timestamp,
+            'data': dataframe for this window
+        }
+    """
+    if interactions_df.empty and price_logs_df.empty:
+        return [], []
+
+    # convert timestamps to datetime
+    interactions_df = interactions_df.copy()
+    price_logs_df = price_logs_df.copy()
+
+    if not interactions_df.empty:
+        if not pd.api.types.is_datetime64_any_dtype(interactions_df[ts_col]):
+            interactions_df[ts_col] = pd.to_datetime(interactions_df[ts_col])
+
+    if not price_logs_df.empty:
+        if not pd.api.types.is_datetime64_any_dtype(price_logs_df[ts_col]):
+            price_logs_df[ts_col] = pd.to_datetime(price_logs_df[ts_col])
+
+    # find global time bounds
+    times = []
+    if not interactions_df.empty:
+        times.extend([interactions_df[ts_col].min(), interactions_df[ts_col].max()])
+    if not price_logs_df.empty:
+        times.extend([price_logs_df[ts_col].min(), price_logs_df[ts_col].max()])
+
+    if not times:
+        return [], []
+
+    earliest = min(times)
+    latest = max(times)
+
+    # create shared time windows
+    windows = pd.date_range(start=earliest, end=latest, freq=window_size)
+
+    if len(windows) < 2:
+        return [], []
+
+    # chunk both datasets
+    interaction_chunks = []
+    price_chunks = []
+
+    for i in range(len(windows) - 1):
+        window_start = windows[i]
+        window_end = windows[i + 1]
+
+        # filter interactions in this window
+        if not interactions_df.empty:
+            mask = (interactions_df[ts_col] >= window_start) & (interactions_df[ts_col] < window_end)
+            interaction_chunk = interactions_df[mask]
+        else:
+            interaction_chunk = pd.DataFrame()
+
+        interaction_chunks.append({
+            'window_start': window_start,
+            'window_end': window_end,
+            'data': interaction_chunk
+        })
+
+        # filter price logs in this window
+        if not price_logs_df.empty:
+            mask = (price_logs_df[ts_col] >= window_start) & (price_logs_df[ts_col] < window_end)
+            price_chunk = price_logs_df[mask]
+        else:
+            price_chunk = pd.DataFrame()
+
+        price_chunks.append({
+            'window_start': window_start,
+            'window_end': window_end,
+            'data': price_chunk
+        })
+
+    return interaction_chunks, price_chunks

experiments/procesing/mapping.py

@@ -0,0 +1,158 @@
+import numpy as np
+import pandas as pd
+from sklearn.base import BaseEstimator, TransformerMixin
+
+def build_transition_prob_matrix(df: pd.DataFrame):
+    df = df.dropna(subset=['eventName'])
+    events = df['eventName'].tolist()
+    labels = pd.Index(events).unique().tolist()
+    idx = {e:i for i,e in enumerate(labels)}
+    M = np.zeros((len(labels), len(labels)), dtype=float)
+    for a, b in zip(events, events[1:]):
+        M[idx[a], idx[b]] += 1
+    row_sums = M.sum(axis=1, keepdims=True)
+    with np.errstate(divide='ignore', invalid='ignore'):
+        P = np.divide(M, row_sums, where=row_sums>0)  # row-normalized
+    return P, labels
+
+# https://medium.com/data-science/time-series-data-markov-transition-matrices-7060771e362b
+from graphviz import Digraph
+import numpy as np
+import pandas as pd
+
+def _as_prob_df(matrix, labels=None):
+    """Return a square DataFrame with index=columns=labels."""
+    if isinstance(matrix, pd.DataFrame):
+        # Ensure square and aligned
+        assert (matrix.index == matrix.columns).all(), "Index/columns must match."
+        return matrix
+    matrix = np.asarray(matrix, dtype=float)
+    assert matrix.shape[0] == matrix.shape[1], "Matrix must be square."
+    if labels is None:
+        raise ValueError("labels are required when matrix is not a DataFrame")
+    assert len(labels) == matrix.shape[0], "labels length must match matrix size."
+    return pd.DataFrame(matrix, index=list(labels), columns=list(labels))
+
+def _df_to_edgelist(P: pd.DataFrame, threshold=0.0, round_digits=2):
+    """Build weighted edges > threshold."""
+    edges = []
+    for src in P.index:
+        for dst in P.columns:
+            w = float(P.loc[src, dst])
+            if w > threshold:
+                edges.append((str(src), str(dst), f"{w:.{round_digits}f}"))
+    return edges
+
+def render_graph(fname, matrix, ls_index=None, threshold=0.0, fmt="svg", view=False):
+    """
+    fname: output file stem (no extension)
+    matrix: NumPy array or pandas DataFrame of transition PROBABILITIES
+    ls_index: ordered labels (required if matrix is not a DataFrame)
+    threshold: hide edges with weight <= threshold
+    fmt: 'svg'|'png'|'pdf' etc.
+    view: open after rendering
+    """
+    P = _as_prob_df(matrix, labels=ls_index)
+    edges = _df_to_edgelist(P, threshold=threshold)
+
+    g = Digraph(format=fmt)
+    g.attr(rankdir="LR", size="30")
+    g.attr("node", shape="circle")
+
+    # ensure isolated nodes appear
+    for node in P.index:
+        g.node(str(node), width="1", height="1")
+
+    for src, dst, label in edges:
+        g.edge(src, dst, label=label)
+
+    g.render(fname, view=view, cleanup=True)
+    return g
+
+
+class TransitionProbMatrixTransformer(BaseEstimator, TransformerMixin):
+    def __init__(self, threshold=0.0):
+        self.threshold = threshold
+        self.P_ = None
+        self.labels_ = None
+
+    def fit(self, X: pd.DataFrame, y=None):
+        P, labels = build_transition_prob_matrix(X)
+        self.P_ = P
+        self.labels_ = labels
+        return self
+
+    def transform(self, X: pd.DataFrame = None):
+        return self.P_, self.labels_
+
+    def render(self, fname: str, fmt="svg", view=False):
+        if self.P_ is None or self.labels_ is None:
+            raise ValueError("Transformer has not been fitted yet.")
+        return render_graph(
+            fname,
+            self.P_,
+            ls_index=self.labels_,
+            threshold=self.threshold,
+            fmt=fmt,
+            view=view
+        )
+
+
+class SessionTransitionProbMatrixTransformer(BaseEstimator, TransformerMixin):
+    def __init__(self, threshold=0.0, session_col='sessionId'):
+        self.threshold = threshold
+        self.session_col = session_col
+        self.session_matrices_ = None
+
+    def fit(self, X: pd.DataFrame, y=None):
+        if self.session_col not in X.columns:
+            raise ValueError(f"Column '{self.session_col}' not found in DataFrame")
+
+        session_matrices = {}
+        for session_id, grp in X.groupby(self.session_col):
+            if len(grp) > 1:  # need at least 2 events for transitions
+                P, labels = build_transition_prob_matrix(grp)
+                session_matrices[session_id] = {'matrix': P, 'labels': labels}
+
+        self.session_matrices_ = session_matrices
+        return self
+
+    def transform(self, X: pd.DataFrame = None):
+        if self.session_matrices_ is None:
+            raise ValueError("Transformer has not been fitted yet.")
+        return pd.Series(self.session_matrices_)
+
+    def render_session(self, session_id: str, fname: str, fmt="svg", view=False):
+        if self.session_matrices_ is None:
+            raise ValueError("Transformer has not been fitted yet.")
+        if session_id not in self.session_matrices_:
+            raise ValueError(f"Session '{session_id}' not found in fitted data.")
+
+        sess_data = self.session_matrices_[session_id]
+        return render_graph(
+            fname,
+            sess_data['matrix'],
+            ls_index=sess_data['labels'],
+            threshold=self.threshold,
+            fmt=fmt,
+            view=view
+        )
+if __name__ == "__main__":
+    # Example usage
+    data = {
+        'eventName': [
+            'A', 'B', 'A', 'C', 'B', 'A', 'A', 'C', 'B', 'C',
+            'A', 'B', 'C', 'A', 'B', 'C', 'A', 'B', 'C', 'A'
+        ]
+    }
+    df = pd.DataFrame(data)
+
+    transformer = TransitionProbMatrixTransformer(threshold=0.1)
+    transformer.fit(df)
+    P, labels = transformer.transform(None)
+
+    print("Transition Probability Matrix:")
+    print(pd.DataFrame(P, index=labels, columns=labels))
+
+    # Render the graph
+    transformer.render("transition_graph", fmt="svg", view=False)

experiments/procesing/pipeline.py

@@ -0,0 +1,90 @@
+from sklearn.pipeline import Pipeline
+from sklearn.preprocessing import StandardScaler
+import pandas as pd
+import logging
+log = logging.getLogger(__name__)
+
+from extract import KafkaDataFetcher, ExperimentJoiner, EventTitleAugmenter, chunk_shared_data
+from mapping import SessionTransitionProbMatrixTransformer, render_graph
+from demand import DemandEstimator, ChunkInteractionsIntoSteps
+from elasticity import TemporalElasticityEstimator, aggregate_price_logs
+
+
+
+# elasticity pipeline components (not sklearn compatible, manual orchestration)
+def elasticity_pipeline(interactions_df, price_logs_df, window_size='30s', store_mode='hotel'):
+    """
+    Compute price elasticity from interaction and price data.
+
+    Args:
+        interactions_df: raw interaction data from demand_data_pipeline
+        price_logs_df: price log data from price_data_pipeline
+        window_size: time window for chunking
+        store_mode: 'hotel' or 'airline'
+
+    Returns:
+        df with [productId, elasticity, std_error, n_obs]
+    """
+    # step 1: chunk interactions into time windows
+    chunker = ChunkInteractionsIntoSteps(window_size=window_size, return_metadata=True)
+    interaction_chunks = chunker.transform(interactions_df)
+    log.info(f"Chunked interactions into {len(interaction_chunks)} windows of size {window_size}")
+
+    if not interaction_chunks:
+        return None
+
+    # step 2: compute demand per window
+    demand_estimator = DemandEstimator(store_mode=store_mode)
+    demand_chunks = []
+    for chunk in interaction_chunks:
+        demand_vector = demand_estimator.transform(chunk['data'])
+        demand_chunks.append({
+            'window_start': chunk['window_start'],
+            'window_end': chunk['window_end'],
+            'demand_vector': demand_vector # each has a full list of all products, even if demand is 0
+        })
+    # [q_chunk1, q_chunk2, ...]
+
+    # step 3: aggregate price logs into windows
+    price_chunks = aggregate_price_logs(price_logs_df, window_size=window_size)
+
+    # step 4: compute elasticity
+    elasticity_estimator = TemporalElasticityEstimator(method='point', min_observations=2)
+    elasticity_df = elasticity_estimator.transform(demand_chunks, price_chunks, store_mode=store_mode)
+
+    return elasticity_df
+
+
+# exposable pipelines
+interaction_pipeline = Pipeline([
+    ('kafka_fetch', KafkaDataFetcher(topic='user-interactions')),
+    ('experiment_join', ExperimentJoiner()),
+    ('event_augment', EventTitleAugmenter()),
+])
+
+price_data_pipeline = Pipeline([
+    ('kafka_fetch', KafkaDataFetcher(topic='price-logs')),
+])
+
+# interaction_data + price_data -> elasticity (demand)
+# elasticity -> pricing
+
+pricing_pipeline = Pipeline([
+    ('demand_estimation', DemandEstimator()),
+])
+if __name__ == "__main__":
+    # fetch both datasets
+    interaction_data = interaction_pipeline.fit_transform(None)
+    pricing_data = price_data_pipeline.fit_transform(None)
+    if interaction_data.empty or pricing_data.empty:
+        print("Insufficient data for elasticity computation"); exit(0)
+    # compute elasticity via unified pipeline
+    window_size = "30s"
+    elasticity_results = elasticity_pipeline(interaction_data, pricing_data, window_size=window_size)
+    elasticity_value_array = elasticity_results['elasticity'].values if elasticity_results is not None else np.array([])
+    print(elasticity_value_array)
+
+    if elasticity_results is not None and not elasticity_results.empty:
+        print(elasticity_results.to_string(index=False))
+    else:
+        print("\nInsufficient data for elasticity computation")

experiments/procesing/pricing.py

@@ -0,0 +1,153 @@
+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 supabase import create_client, Client
+from pipeline import interaction_pipeline, price_data_pipeline, elasticity_pipeline
+
+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  # simple coefficient
+
+    def fit(self, historical_data):
+        return self
+
+    def transform(self, state_space: StateSpace) -> np.ndarray:
+        # Simple linear adjustment: P_{t+1} = P_t + sensitivity * Q_t
+        new_prices = state_space.prices + self.price_sensitivity * state_space.demand # this is not great
+        return np.maximum(new_prices, 0)
+
+# Example usage:
+if __name__ == "__main__":
+    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/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()

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 "\\subsection{${escaped_path}}" >> "$OUTPUT_FILE"
-    echo "\\begin{lstlisting}[caption={${relpath}}]" >> "$OUTPUT_FILE"
+    echo "\\begin{lstlisting}[caption={${escaped_path}}]" >> "$OUTPUT_FILE"
-    cat "$filepath" >> "$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))
+    '("footnotetextcopyrightpermission" 1)))
-   (LaTeX-add-labels
-    "research"))
  :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}
+\input{chapters/01-intro}
-
+\input{chapters/02-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}.
+\input{chapters/03-methodology}
-
+\input{chapters/04-results}
-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.
+\input{chapters/05-discussion}
-
+\input{chapters/06-conclusion}
-
-\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.
 
 
 \printbibliography
 
-% \clearpage
+\clearpage
-% \onecolumn
+\onecolumn
-% \appendix
+\appendix
-
+\input{../build/concatenated_code}
 
 \end{document}

pytest.ini

@@ -0,0 +1,7 @@
+[pytest]
+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';
+  subject_name: string;
-  sessionIds: string[];
+  xp_human_only: boolean;
-  createdAt: number;
+  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 () => {
+  const handleExperimentCreated = async () => {
-    if (!sessionId) {
+    setShowForm(false);
-      setError('no session available');
+    setSelectedTaskId(undefined);
-      return;
+    await fetchExps();
-    }
-
-    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 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="mx-auto max-w-7xl">
-        <div className="mb-8 flex items-center justify-between">
+        <div className="mb-8">
-          <div>
+          <h1 className="text-3xl font-semibold tracking-tight text-black dark:text-zinc-50">
-            <h1 className="text-3xl font-semibold tracking-tight text-black dark:text-zinc-50">
+            Experiment Management
-              Experiments
+          </h1>
-            </h1>
+          <p className="mt-2 text-sm text-zinc-600 dark:text-zinc-400">
-            <p className="mt-2 text-sm text-zinc-600 dark:text-zinc-400">
+            configure tasks and run experiments
-              current session: {sessionId || 'none'}
+          </p>
-            </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>
 
         {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">
+        <div className="grid grid-cols-1 gap-6 lg:grid-cols-3">
-          <table className="w-full text-left text-sm">
+          {/* left column: task manager */}
-            <thead className="border-b border-zinc-200 bg-zinc-50 dark:border-zinc-800 dark:bg-zinc-900">
+          <div className="lg:col-span-1">
-              <tr>
+            <TaskManager
-                <th className="px-6 py-3 font-medium text-zinc-900 dark:text-zinc-100">
+              onTaskSelect={setSelectedTaskId}
-                  experiment id
+              selectedTaskId={selectedTaskId}
-                </th>
+            />
-                <th className="px-6 py-3 font-medium text-zinc-900 dark:text-zinc-100">
+          </div>
-                  status
+
-                </th>
+          {/* right column: experiment form + list */}
-                <th className="px-6 py-3 font-medium text-zinc-900 dark:text-zinc-100">
+          <div className="space-y-6 lg:col-span-2">
-                  session count
+            <div className="flex items-center justify-between">
-                </th>
+              <h2 className="text-lg font-semibold text-zinc-900 dark:text-zinc-100">
-                <th className="px-6 py-3 font-medium text-zinc-900 dark:text-zinc-100">
+                Experiments
-                  created
+              </h2>
-                </th>
+              <button
-                <th className="px-6 py-3 font-medium text-zinc-900 dark:text-zinc-100">
+                onClick={() => setShowForm(!showForm)}
-                  action
+                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"
-                </th>
+              >
-              </tr>
+                {showForm ? 'hide form' : 'new experiment'}
-            </thead>
+              </button>
-            <tbody className="divide-y divide-zinc-200 dark:divide-zinc-800">
+            </div>
-              {exps.length === 0 ? (
+
-                <tr>
+            {showForm && (
-                  <td
+              <ExperimentForm
-                    colSpan={5}
+                selectedTaskId={selectedTaskId}
-                    className="px-6 py-8 text-center text-zinc-500 dark:text-zinc-400"
+                onSuccess={handleExperimentCreated}
-                  >
+              />
-                    no experiments yet
+            )}
-                  </td>
+
-                </tr>
+            <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">
-                exps.map((exp) => (
+                <thead className="border-b border-zinc-200 bg-zinc-50 dark:border-zinc-800 dark:bg-zinc-900">
-                  <tr
+                  <tr>
-                    key={exp.id}
+                    <th className="px-4 py-3 font-medium text-zinc-900 dark:text-zinc-100">
-                    className="hover:bg-zinc-50 dark:hover:bg-zinc-900"
+                      subject
-                  >
+                    </th>
-                    <td className="px-6 py-4 font-mono text-xs text-zinc-700 dark:text-zinc-300">
+                    <th className="px-4 py-3 font-medium text-zinc-900 dark:text-zinc-100">
-                      {exp.id.slice(0, 8)}...
+                      mode
-                    </td>
+                    </th>
-                    <td className="px-6 py-4">
+                    <th className="px-4 py-3 font-medium text-zinc-900 dark:text-zinc-100">
-                      <span
+                      human
-                        className={`inline-block rounded-full px-2 py-1 text-xs font-medium ${
+                    </th>
-                          exp.status === 'active'
+                    <th className="px-4 py-3 font-medium text-zinc-900 dark:text-zinc-100">
-                            ? 'bg-green-100 text-green-800 dark:bg-green-950 dark:text-green-200'
+                      task
-                            : 'bg-zinc-100 text-zinc-800 dark:bg-zinc-800 dark:text-zinc-200'
+                    </th>
-                        }`}
+                    <th className="px-4 py-3 font-medium text-zinc-900 dark:text-zinc-100">
-                      >
+                      created
-                        {exp.status}
+                    </th>
-                      </span>
+                    <th className="px-4 py-3 font-medium text-zinc-900 dark:text-zinc-100">
-                    </td>
+                      link
-                    <td className="px-6 py-4 text-zinc-700 dark:text-zinc-300">
+                    </th>
-                      {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>
                   </tr>
-                ))
+                </thead>
-              )}
+                <tbody className="divide-y divide-zinc-200 dark:divide-zinc-800">
-            </tbody>
+                  {exps.length === 0 ? (
-          </table>
+                    <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';
+function FlightsList() {
-type FareRule = 'flexible' | 'standard' | 'basic';
+  const searchParams = useSearchParams();
+  const [flights, setFlights] = useState<Flight[]>([]);
+  const [loading, setLoading] = useState(true);
+  const [error, setError] = useState<string | null>(null);
 
-interface Flight {
+  useEffect(() => {
-  id: string;
+    const fetchFlights = async () => {
-  departure: { time: string; airport: string };
+      try {
-  arrival: { time: string; airport: string };
+        const url = new URL('/api/products', window.location.origin);
-  duration: string;
+        url.searchParams.set('type', 'airline');
-  stops: number;
-  cabinClass: CabinClass;
-  fareRule: FareRule;
-  refundable: boolean;
-  basePrice: number;
-}
 
-const genRandomFlights = (): Flight[] => {
+        // forward all relevant search params to the API
-  const airports = ['JFK', 'LAX', 'ORD', 'ATL', 'DFW', 'SFO', 'SEA', 'MIA'];
+        const params = ['dateIndex', 'origin', 'destination', 'tripType', 'adults', 'children', 'infants'];
-  const cabins: CabinClass[] = ['economy', 'premium', 'business', 'first'];
+        params.forEach(param => {
-  const fareRules: FareRule[] = ['flexible', 'standard', 'basic'];
+          const val = searchParams.get(param);
+          if (val) url.searchParams.set(param, val);
+        });
 
-  return Array.from({ length: 12 }, (_, i) => {
+        const res = await fetch(url.toString());
-    const depHour = Math.floor(Math.random() * 24);
+        if (!res.ok) throw new Error(`Failed to fetch: ${res.status}`);
-    const arrHour = (depHour + Math.floor(Math.random() * 6) + 2) % 24;
+        const json = await res.json();
-    const stops = Math.random() > 0.6 ? 0 : Math.floor(Math.random() * 2) + 1;
+        const transformed = json.data.map((p: AirlineProduct) => transformProduct(p));
-    const cabin = cabins[Math.floor(Math.random() * cabins.length)];
+        setFlights(transformed);
-    const fareRule = fareRules[Math.floor(Math.random() * fareRules.length)];
+      } catch (e) {
-
+        setError(e instanceof Error ? e.message : 'Failed to load products');
-    const basePrice = Math.floor(
+        console.error('[FETCH_ERROR]', e);
-      (cabin === 'economy' ? 200 : cabin === 'premium' ? 400 : cabin === 'business' ? 800 : 1500) +
+      } finally {
-      Math.random() * 300
+        setLoading(false);
-    );
+      }
-
-    return {
-      id: `flt-${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,
     };
-  });
+    fetchFlights();
-};
+  }, [searchParams]);
-
-export default function AirlineProducts() {
-  const flights = genRandomFlights();
 
   return (
     <>
-      <Navigation />
+      <h1 className="text-3xl font-bold mb-6">Available Flights</h1>
-      <main className="max-w-7xl mx-auto px-4 py-8">
+      {loading && <div className="text-center py-8">Loading...</div>}
-        <h1 className="text-3xl font-bold mb-6">Available Flights</h1>
+      {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 { NextRequest, NextResponse } from 'next/server';
-import { getAllExperiments } from '@/lib/sessionStore';
+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();
+    const cookieStore = await cookies();
-    return NextResponse.json({ experiments: exps });
+    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' },
@@ -34,11 +23,46 @@ 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();
+
+    // 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);
+            // don't fail the pricing request if logging fails
+        }
+    }
+
+    // log in dev
+    if (process.env.NODE_ENV === 'development') {
+        console.log('[pricing-api]', {
+            productId,
+            sessionId,
+            experimentId,
+            storeMode,
+            price,
+            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 {
+function RoomsList() {
-  id: string;
+  const searchParams = useSearchParams();
-  name: string;
+  const [rooms, setRooms] = useState<Hotel[]>([]);
-  roomType: string;
+  const [loading, setLoading] = useState(true);
-  checkIn: string;
+  const [error, setError] = useState<string | null>(null);
-  checkOut: string;
+
-  amenities: string[];
+  useEffect(() => {
-  refundable: boolean;
+    const fetchRooms = async () => {
-  pricePerNight: number;
+      try {
-  nights: number;
+        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>
+        <Suspense fallback={<div className="text-center py-8">Loading...</div>}>
-        <div className="space-y-4">
+          <RoomsList />
-          {hotels.map((h) => (
+        </Suspense>
-            <HotelCard key={h.id} hotel={h} />
-          ))}
-        </div>
       </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="grid grid-cols-1 sm:grid-cols-2 lg:grid-cols-3 gap-4">
-            <div className="sm:col-span-2">
+            <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>
+              <Label htmlFor="guests">Guests</Label>
-              <DateInput
+              <Dropdown label={`${guests.adults} ${guests.adults === 1 ? 'adult' : 'adults'}`}>
-                id="checkOut"
-                value={checkOut}
-                onChange={(e) => setCheckOut(e.target.value)}
-                required
-              />
-            </div>
-
-            <div className="sm:col-span-2 lg:col-span-4">
-              <Label htmlFor="guests">Guests & Rooms</Label>
-              <Dropdown label={`${guests.adults} ${guests.adults === 1 ? 'adult' : 'adults'}, ${guests.rooms} ${guests.rooms === 1 ? 'room' : 'rooms'}`}>
                 <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
+                    }
+                },
+            },
+        },
+    );
+};