An internal knowledge base your AI can actually talk to
Answers grounded in your own documents and data, with a citation on every claim. We design, ship and tune the retrieval layer that makes that possible in production.
Generic AI hallucinates. Yours shouldn't.
Your organisation's knowledge is spread across documents, wikis, databases, tickets and inboxes. Finding the right answer is slow, and new starters spend their first weeks learning where things live rather than doing the work.
A general-purpose model has read none of it. Ask it anyway and it will guess. A retrieval layer that genuinely helps takes real engineering: document parsing, chunking, embeddings, a vector index, reranking, and prompts that force the model to cite what it used.
Get that wrong and you ship an AI that is confidently incorrect — worse than shipping nothing at all.
Institutional knowledge sits in dozens of systems that never talk.
A general-purpose model cannot cite what it has never read.
Chunking and embedding choices decide what can be found at all.
Retrieval quality caps answer quality, so reranking is not optional.
Permissions have to survive the trip into the index.
Indexes go stale, and keeping them fresh is ongoing operational work.
How we build it
A retrieval pipeline that returns the right passage, and answers that show their sources.
Document processing
We ingest PDFs, Word files, wikis, web pages and databases, then chunk them so every retrieved passage still carries the context around it.
Semantic search
Vector search plus reranking, so a plainly worded question finds the right passage even when the two share no keywords.
Multi-source synthesis
Answers that draw on several documents and systems at once, with each claim traced back to the source it came from.
Access control
Your existing roles are enforced at retrieval time, so a user only ever sees passages from documents they could already open.
Keeping it current
New and changed records are re-indexed automatically, so an answer reflects today's data rather than the day you launched.
Enterprise security
Data stays inside your environment. Encrypted at rest and in transit, with full audit logging for compliance reviews.
Every engagement includes
- Ingestion and document-processing pipeline in your repo.
- Vector database chosen, deployed and tuned.
- Semantic search with reranking and evaluated retrieval quality.
- Conversational question-and-answer interface.
- Source citations on every answer.
- Role-based access control enforced at retrieval time.
- Automatic re-indexing as your content changes.
- Monitoring, analytics and a working session with your team.
Ready to put your own data behind your AI?
Bring the questions your team keeps asking to a free 30-minute call, and we'll scope what it takes to answer them from your own documents.