PHANTOM/docs/src/roadmap.md

Roadmap & implementation notes

This page is the honesty pass from the documentation plan: what clients can expect today versus what remains research-heavy.

Turnkey in this repository

• Local stack: Docker Compose services for backend, Kafka, Redis, Airflow, pricing provider, etc.; Next.js via make web.dev (see Platform setup).
• Demo verticals: hotel and airline storefront modes.
• Engine: Benchmarks and training entrypoints (make train, make benchmark), KL-based agent scoring in [engine/lib/coi.py](https://github.com/velocitatem/PHANTOM/blob/main/engine/lib/coi.py), simulator mixing in [engine/engine.py](https://github.com/velocitatem/PHANTOM/blob/main/engine/engine.py).
• Orchestration hooks: Ray/TPU scripts (submit_ray_job.sh, make tpu.ray.*), W&B sweep agents, Docker trainer publish target.

Usually requires custom engineering

• Non-Supabase catalog or checkout flows without adapting the web + backend contracts.
• Production SLAs on Kafka, schema registry, or PII boundaries for your jurisdiction.
• Tight coupling to a legacy pricing engine without mapping its API to the provider abstraction.

Thesis vs code

• The thesis states theorems and constructions (COI erosion, kernels, \mathcal{G}(\alpha), DR-RL).
• The codebase implements a subset of that story for experiments: verify CLI flags and simulator assumptions before claiming 1:1 equivalence with every equation.
• Catalog-scale kernel expansion is discussed in Chapter 3 with explicit validation caveats—do not assume row-stochasticity and Markov structure are automatically preserved at full product cardinality without review.

Suggested client messaging

Position PHANTOM as a reproducible research and evaluation stack for agent-aware pricing, with a path to custom integration—not as a black-box “turn on anti-agent pricing” product without data and engineering investment.