How BizQuery works

BizQuery is built so you can trust what the AI tells you. Three ideas make that possible.

Grounded answers, not guesses

When you ask a question, the advisor doesn’t answer from a model’s general training. It searches your documents for the most relevant passages and answers from those. This is retrieval‑augmented generation (RAG): the model reasons, but over your evidence.

If your knowledge base has nothing relevant to a question, a good advisor says so rather than inventing an answer.

Citations you can check

Every answer carries its evidence — the passages and documents the advisor pulled in, with the tool calls it made. You can expand it to confirm a claim came from your source material and not from somewhere else.

Human sign-off for decisions

A question is one thing; a decision is another. Proposals implement maker‑checker: the AI drafts a recommendation (the maker), and a human approves, refines, or rejects it (the checker) before it counts. The decision, the reasoning, and the reviewer are all recorded for audit.

Choosing the model

Where your deployment offers a choice, a model picker lets you pick the chat model per conversation — a fast model for simple lookups, a stronger reasoning model for analysis. An auto option can route each question to the right one for you.

Note

Your data stays yours. BizQuery is designed to run against your own document store and your chosen model endpoint — answers are grounded in your sources, not pooled with anyone else’s.