Laminae turns your documents into source-attributed, MCP-native knowledge bases any AI client can use. Managed cloud or self-hosted — same code, your choice.
Upload documents into a bucket. Spin up an MCP server. Point Claude Desktop, Cursor, or your own copilot at it. Laminae returns source-attributed evidence; your AI client does the talking.
DeploymentHow it works
Managed cloud, self-hosted, or air-gapped — same product, your call
Run Laminae as a managed per-tenant deployment, or take the same Docker Compose stack and run it on your own infrastructure. The wedge is open-standard, so the choice never locks you in.
Buckets and MCPsHow it works
Upload to a bucket, expose it as an MCP server, done
Buckets are managed, vectorised knowledge stores. MCP servers are how your AI client talks to them. Non-technical teams can spin up both in an afternoon — no data team required.
Agentic retrievalHow it works
Hybrid retrieval, source-attributed, no summarising layer
Dense plus sparse plus re-ranked. Every chunk is returned with its source document, page, and snippet, so the AI client — and the human reading along — can always verify the answer.
Most companies have the documents. Most companies have an AI client their team already likes. The missing piece is the bit in the middle — a retrieval layer that's honest about what it knows and where it got it.
Every time a team member embeds a document into an AI chat, tokens are consumed. Multiply that by team size, daily frequency, and document volume — and costs compound fast. Adjust the variables below to see the spiral in real time.