Knowledge assistants
Answers drawn from your own documents, with citations back to the source, so people can verify rather than trust.
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The knowledge that exists but cannot be found
Every company holds the answers to its own questions somewhere: technical manuals, past project documentation, policies, contracts, specifications, the long email thread where something was finally decided. The problem is never that the knowledge is absent. It is that finding it takes longer than asking a colleague, so people ask the colleague, and a handful of experienced people become a permanent bottleneck. A knowledge assistant makes that searchable by meaning rather than by keyword, which matters because people rarely remember the exact words a document used.
Retrieval augmented generation, done carefully
Documents are chunked, embedded and indexed. A question retrieves the most relevant passages and the model answers from those, citing which document it used. The citation is essential: without it you have something confident and unverifiable, which is worse than nothing in a professional context. Permissions are enforced at retrieval, so the assistant cannot surface a document the asker could not open directly. This sounds obvious and is frequently got wrong, usually with expensive consequences involving salary data or HR files.
Knowing what it does not know
The failure mode that destroys trust is a confident answer built from passages that were not actually relevant. So we tune for the assistant to say it could not find an answer rather than to construct one, and we measure that behaviour explicitly in the evaluation set. Users forgive an assistant that says "I do not have that". They stop using one that has been wrong twice while sounding certain. Getting that balance right is most of the engineering.
What you get
Search by meaning
Finds the right passage even when the question uses none of the words the document does.
Every answer cites its source
People can verify rather than trust. Without citations you have something confident and unverifiable.
Permission-aware retrieval
It cannot surface a document the person could not open directly. Frequently got wrong, expensively.
Says when it does not know
Tuned and measured to decline rather than construct. Users forgive that; they do not forgive confident errors.
Any document source
SharePoint, network drives, PDFs, Confluence, ticket history and databases, indexed together.
Stays current
Re-indexed as documents change, so it does not confidently quote a policy you replaced last year.
Technologies
Frequently asked questions
How is this different from Microsoft 365 Copilot?
Copilot works across your Microsoft 365 content and is excellent at that. A custom knowledge assistant makes sense when the knowledge lives outside Microsoft 365, when you need specific retrieval behaviour or citation formats, when it must be embedded in your own product, or when you need a particular model. Often Copilot is the better answer and we will say so.
What if it gives a wrong answer?
Citations make errors visible, which is the point. We also tune it to decline rather than guess, and the evaluation set measures that behaviour with your real questions. It will still occasionally be wrong, and it should be positioned to users as a fast first stop, not an authority.
How many documents can it handle?
Volume is rarely the constraint; modern vector databases handle very large corpora. Quality is the constraint. Ten thousand documents including five outdated versions of the same policy produce worse answers than a curated two thousand, so we usually start by looking at what should be indexed at all.
Make your own documents answerable
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