Document Intelligence Pipeline

Real documents are messy — scanned PDFs, nested tables, inconsistent structure. This pipeline is our reference for turning that mess into something a model can reason over reliably.

The pipeline

1. Parse — layout-aware extraction that preserves tables and headings.
2. Chunk — semantic boundaries, not fixed token counts, with metadata carried through.
3. Index — hybrid retrieval over the chunks, versioned so you can roll back.
4. Extract — grounded, cited extraction with an explicit "not found" path.

Why it matters

The chunking and grounding discipline here is exactly what we argue for in Production RAG: Beyond the Demo. This project is that argument made concrete.

Status

Concept and reference implementation. Have a document-heavy problem? Let's talk.