How I built a document knowledge platform around measured retrieval, verifiable citations, explicit failure states, tests, CI, and architecture decisions.
Public source, tests, CI, ADRs, evaluation sets, and a locally runnable product. No hosted demo yet.
01 / Overview
A document knowledge product built to make answer quality inspectable. It ingests PDFs, preserves page provenance, measures lexical retrieval, and returns grounded answers whose citations are resolved from trusted server-side evidence.
02 / The problem
The problem
Teams need answers from policies, contracts, runbooks, and research that readers can verify; fluent text without attributable evidence is not a trustworthy product outcome.
03 / My responsibility
My responsibility
Defined the product states and milestone sequence, then implemented each vertical slice across the Next.js frontend and FastAPI backend.
Designed ingestion, extraction, retrieval, answer generation, citations, evaluation sets, tests, CI, and architecture decision records.
Kept implementation status separate from live-provider acceptance and documented missing capabilities explicitly.
Constraints
Constraints
The first useful slice had to run locally without cloud infrastructure, background workers, or a hosted database.
Every answer citation had to resolve to a page and passage the server actually retrieved.
Provider credentials could not be assumed, so offline gates and live answer acceptance needed separate definitions.
04 / Architecture
Architecture
Current request path
The browser drives explicit upload, extraction, indexing, search, and answer states. The API owns validation and provenance; SQLite is a rebuildable lexical index; the model receives only bounded retrieved evidence.
The résumé provides the complete career context. For client-sensitive details, contact me and I can discuss the work at the appropriate disclosure level.