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# Business overview
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PHANTOM targets **platform operators and researchers** who need to:
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1. **Observe** session-level behavior and price quotes together (trajectories and policies—not just clicks).
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2. **Separate** human-driven demand signals from agent-mediated reconnaissance where possible (distinguishability and contamination \alpha in the thesis).
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3. **Evaluate** pricing policies that remain useful when **Cost of Information (COI)** is under pressure from automated querying (formal COI framework and theorem in the thesis PDF).
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## What this product is not
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- A drop-in fraud API that returns “bot score” for every request without your event schema.
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- A certified compliance guarantee for regulated pricing: it is a **research stack** with configurable experiments.
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- A hosted SaaS: you run the stack (or adapt components) under your infrastructure policy.
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## Self-service story (ideal path)
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A team connects their **catalog** (today: Supabase-backed flows in this repo), streams **interaction events** through the ingest path, runs **labeled or weak-labeled** human vs agent sessions, estimates **behavioral kernels**, varies **contamination** in simulation, and **trains or benchmarks** robust policies via `engine/`. Steps and caveats are in [Setup](platform-setup.md) (same content as root `SETUP.md`).
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## Thesis link
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Problem statement, contributions, and research questions: **Introduction** and abstract in the [thesis PDF](https://pub-d5b94a3c29fd40c6b3881946e463fdb7.r2.dev/thesis-latest.pdf).
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