AI knowledge systems
We connect your manuals, specifications, policies and past work to search and question answering that cites its sources and respects who is allowed to see what.
When you need this
- Answers exist somewhere in thousands of documents, but finding them takes too long.
- Experienced staff are interrupted with the same questions every day.
- New hires take months to learn where information lives.
- You need answers people can verify, not confident guesses.
How we do it
1. Collect real questions
Gather the questions people actually ask, and which documents should answer them.
2. Ingest and index
Document processing, chunking and indexing, with update handling so new and changed documents stay current.
3. Permissions and citations
Answers draw only on documents the user may see, and every answer links to its sources.
4. Evaluate
Measure retrieval and answer quality against the real questions before launch, and keep the test set for later changes.
What you get
- Ingestion and indexing pipeline with update handling
- Search and answers with citations
- Access permissions matched to your existing groups
- Evaluation set and quality report
An engineering team searches manuals, specifications and previous projects in plain language. Each answer quotes the passage it relies on and links to the page, so engineers can check it before they act on it.
Start with a project conversation
Scoped to your project
Tell us what you want to build. We reply to set up a short conversation. A clear project can be scoped directly; if the solution is not yet clear, solution design comes first.
Questions
Will it make things up?
It is designed to answer only from your documents and to say when it cannot find an answer. We measure how often it gets this right before launch.
Where are our documents stored?
In an environment agreed for your project: your cloud account, a private deployment or a managed service, depending on your requirements.
Often combined with
- Custom AI ImplementationTailored AI systems, agents and integrations connected to your data, tools and operating environment.
- Private AI infrastructureRun models and data where your requirements say they must run.
- AI evaluation and Reliability AuditMeasure how well an AI system already in use works, and fix what is failing.