A retrieval system over proprietary knowledge

Institutional knowledge lived across documents that were technically accessible but practically unsearchable: finding an answer meant knowing which document held it and who to ask.

A generic chatbot wasn't an option — answers had to come from the client's actual material, and wrong-but-confident answers would be worse than none.

We build RAG systems as measured pipelines, not demos: ingestion and indexing tuned to the client's document types, retrieval evaluated against a test set drawn from real questions, and answers that cite their sources.

Grounding is enforced structurally — the system answers from retrieved material and says so when the material isn't there, rather than improvising.

Biotite built a retrieval-augmented pipeline over the client's proprietary documents: ingestion, chunking, and indexing shaped to how the material is actually written, with retrieval and answer synthesis that returns cited passages alongside every response.

An evaluation harness measures retrieval and answer quality against known cases, so changes to the system are tested rather than vibes-checked.

Questions that previously routed through specific people or manual document searches can now be answered directly from the knowledge base, with citations to verify.

The client can see how well the system performs — and how changes affect it — instead of trusting it blindly.