Retrieval that cites its source, chunk by chunk

An answer that sounds right and an answer that is right look identical in a chat window. That's the core problem with retrieval done badly — the model fills gaps with something plausible, and nobody notices until it's wrong in a way that matters.

Cortez AI's knowledge pipeline chunks every uploaded document — PDF, DOCX, CSV, Markdown, or a crawled page — and keeps each chunk tied back to its source file and position. When an agent answers using retrieved context, that lineage travels with it, so a reply can be checked against the actual document it came from instead of taken on faith.

It's a small architectural decision that changes how much you can trust the thing in production: a support agent that cites your refund policy verbatim is a very different product from one that improvises a refund policy that sounds right.