Building layout-assisted field extraction
Reuse the layout map already stored at index time, match each extracted value back to a page region, and return…
Make AI answers auditable
A valid extract still isn't enough if nobody can open the page and see where each field came from.
Building schema-first extraction with validators
Strict JSON Schema at the model boundary, optional business rules after it, and an audit trail that survives the handoff.
Mostly-right data extraction isn’t operational data
Soft JSON from document extraction passes the demo; operational data needs a declared shape and rules you can fail.
Building registry-based extraction for any document type
Two-level discovery and a tenant allowlist in front of lookup are how a new document type, or a new field…
One extraction engine, any document type
A new document type should be configuration, and two customers sharing that type still need their own complete field set,…
How a document workflow engine differs from a chat service
The moment the output contract changes from free text to validated data, state, control flow, and the API all flip,…
Chat is not enough when the job is a process
A great chat reply still leaves the real work on your desk, and knowing where conversation stops is what separates…
Building agentic RAG that grounds every answer
How a tool-calling loop, hybrid retrieval, and tracked citations turn a chatbot into an answer you can verify
What a good AI answer actually looks like
Why grounding, citations, and agentic tool use separate a trustworthy answer from a fluent one