An AI that knows your company

Most "AI knowledge" projects don't fail at the model - they fail at the source. The company has six versions of the same policy, three Confluence spaces nobody owns, and a SharePoint that mirrors an outdated Drive. Retrieval engineering is the easier half of the work; the harder half is helping the organization decide what is actually authoritative.

Done right, this becomes the most quietly valuable system in the company. New hires onboard faster. Customer support cites policy correctly. Engineering finds the architecture decision record from three years ago in seconds instead of asking the one person who remembers. And the legal team gets answers grounded in passages they can verify, not paragraphs that "sound about right."

The non-negotiable in regulated industries is that the system tells the truth about its own knowledge. Citations on every answer, refusal when the source is missing, and an audit trail of what was retrieved and shown - by design, not as a feature flag. That posture is what separates a knowledge system you can defend to a regulator from a chatbot you can't.