Who it's for
This service fits teams that lose time searching across shared drives, intranets, tickets and long documents. It is especially useful when the right answer exists but is hard to find, changes over time or depends on a person's access level.
- Support teams looking up policies, product details and known solutions while helping customers
- Operations teams searching procedures, contracts, project records or technical documentation
- Growing businesses whose important knowledge is spread across folders and experienced employees
What we build
The technical pattern is often called retrieval-augmented generation, or RAG. Before the AI answers, it searches an approved collection and retrieves the passages most relevant to the question. The answer cites those passages instead of relying only on the model's general memory.
Useful knowledge systems need more than a chat box. We define which source is authoritative, preserve document permissions, show when information was last updated and make “I don't know” an acceptable answer. We also create a repeatable test set so answer quality can be checked after documents, models or settings change.
How we work
- Map the knowledge — We identify the questions people ask, the source systems and who may see each collection.
- Prepare the sources — We remove duplicates, identify owners and define how updates reach the index.
- Build and evaluate — We configure retrieval, citations and answer rules, then test on real questions and difficult cases.
- Launch and improve — We release to a small group, review unanswered questions and improve the sources or retrieval where evidence shows a gap.
What you get
- Knowledge map — The approved sources, owners, update paths and access rules
- Search and answer experience — A secure web, Teams, Slack or application interface suited to your staff
- Cited answers — Links to the exact documents or records supporting each response
- Evaluation set and results — Real questions, expected evidence and measured answer quality
- Usage and gap reporting — Visibility into unanswered questions, weak sources and content that needs an owner
Privacy and permissions come first
The assistant should never reveal a document merely because it found it. We carry source permissions into the retrieval design, minimize the text sent to a model and document every outside service involved. Sensitive collections can use a private cloud or local model when the business case justifies it.
The system also needs a maintenance owner. Old policies produce confidently outdated answers, so each collection gets an update path and a visible freshness rule. If the assistant will take action after finding an answer, we connect it to a controlled AI agent rather than hiding action logic inside search.