Author
How PolicyTrace designs prompts as one bounded layer in a reviewable Document AI system: document classification, specialist extraction, typed outputs, field citations, and evaluation gates.
How I would evaluate PolicyTrace beyond simple accuracy: golden examples, conflict fixtures, provenance checks, review outcomes, regression gates, and operational metrics.
A production hardening roadmap for PolicyTrace: authentication, durable storage, background jobs, audit trails, evaluation, monitoring, retention, and operational ownership.
How PolicyTrace packages a FastAPI backend and React review UI into one Docker app for a Hugging Face Space, and what still separates a public demo from production deployment.
How PolicyTrace turns extraction into a reviewer workflow with split-screen PDF evidence, field highlighting, confidence signals, verification, flags, and overrides.
How PolicyTrace turns overlapping insurance PDFs into a reviewable Golden Record by separating extraction from arbitration, conflict handling, evidence, and human review.
Why AI evaluation is not a report card after launch, but a design constraint from day one.
A practical framework for making AI outputs reviewable, traceable, and trustworthy in real workflows.
A layered architecture map for PolicyTrace, showing where parsing, privacy, model calls, evidence artifacts, arbitration, and human review belong.
A compact 8-check framework for deciding whether an AI idea deserves a production workflow, before you spend weeks building around it.
A visual guide to the gap between a working AI demo and a production workflow that survives real inputs, users, review, cost, and failure.
