Answers from your own documents

Most companies already have the answers their people need, in manuals, procedures and old tickets nobody can search. Plenty have tried a chatbot over them, and many of those pilots stall the first time it quotes a document someone wasn't allowed to see. This is how an assistant on Databricks finds the right page and keeps to the rules.

On the night shift, a technician needs the torque setting for a pump seal on Line 3. It's somewhere in a four hundred page manual, two revisions old, and the engineer who knew it by heart retired last spring.

A general chatbot will answer anyway. It has never read your manual, so it gives a confident number from somewhere else, and nobody on the floor can tell the difference.

So the assistant looks before it answers. The manuals go into Unity Catalog, ai_parse_document reads the text and the tables out of every page, and an AI Search index keeps up on its own whenever someone revises a manual.

The answer comes back with the manual and the page it used, so the technician can check it in a few seconds before touching the pump.

Then there's who sees what. Unity Catalog decides who can search the index at all. The index won't hide one document from one person by itself, though, so we tag every passage with its site and role, and filter on that for every search.

And before anyone on the floor relies on it, we score it on real questions with known answers, using MLflow's judges, and score it again every time the model, the prompt or the index changes.

That's what we build, and it's where most of these projects stall. We start from the questions people really ask, carry each source's permissions into the index, and answer questions about numbers from the system that owns them, with the query on screen. If your people spend their nights searching PDFs, tell us which documents they search most, and one of our senior engineers will show you what an assistant over them would answer.

We're a Databricks partner, with 80 Databricks-certified engineers.