Private AI and knowledge systems

The problem

Knowledge sits in inboxes, shared drives and people's heads. The same questions are answered again and again, and a public chatbot is not an option because the material is confidential or the answer has to be traceable to a source.

What it looks like

Generic examples of the kind of work. They are not client results.

• Internal policy and procedure questions

  A team asks how a procedure works and gets an answer that points to the passage it came from, so a person can check it.

• Support answers from a controlled library

  Draft replies built only from approved documents, with unanswered questions reported as gaps instead of guessed.

• Search across scattered files

  One place to find what the documents say, respecting who is allowed to read each one.

What you receive

• ✓A written data boundary: what is extracted, embedded, retrieved, sent to a model, logged and backed up, and where each stage runs
• ✓Document ingestion and retrieval over the sources you approve
• ✓Access control that follows the permissions you already have
• ✓Answers that reference their source passages, and an explicit way to say "not found in the documents"
• ✓An evaluation set built from your own questions, and a report of how the system did on it
• ✓Documentation for whoever operates it

The pilot and how we judge it

One document collection and one group of users, with questions your team actually asks.

Acceptance criteria, written before the pilot starts:

• ✓Success criteria are written down before the pilot starts, using your own questions.
• ✓Questions the documents cannot answer are reported as unanswered, not answered anyway.
• ✓A reviewer can follow any answer back to the passage it used.
• ✓Someone without permission to a document cannot retrieve it through the assistant.

Boundaries and honest limits

• Retrieval does not train your documents into a model. It looks passages up at question time.
• Running a model on a server you own is one part of privacy. Extraction, embeddings, logs and backups matter too.
• We do not promise perfect accuracy or zero hallucinations. The pilot measures how it behaves on your material.
• Private GPU inference can need different hardware than a standard virtual server. We check that during scoping.