Answers grounded in your docs.
Upload files, documents, and URLs. ConnectLabs chunks and embeds them into a vector index, then your agents retrieve and cite the right passage mid-conversation — never guessing, always anchored to your content.
Sources become chunks, chunks become vectors. Agents search the index and cite the passage they used.
Upload, embed, retrieve
From raw source to cited answer — the path every question takes.
Bring your sources
Add a knowledge base from a file, a document, or a URL. Each upload becomes one tenant-scoped entry your agents can draw from.
Chunk & embed
We split every source into passages and embed them into a vector index — 1536 dimensions, filtered by knowledge base and organization.
Retrieve & cite
Mid-conversation, agents run semantic search over the index, pull the most relevant passage, and ground their answer in it.
Built for grounded answers
Everything the knowledge base does to keep agent answers accurate and on-source.
Multiple source types
Each knowledge base entry is a File, a Document, or a URL — mix and match however your content lives.
Semantic vector search
Retrieval runs over a vector index by embedding, so agents match on meaning rather than keyword overlap.
Per-agent assignment
A join table maps knowledge bases to specific agents, so every agent answers from exactly the sources you intend.
Grounded & citable
Answers trace back to the retrieved passage, so responses stay anchored to your content instead of guessing.
Multi-tenant isolation
Every entry and chunk is scoped by organization. Retrieval filters by org, so tenants never see each other's data.
Always up to date
Re-upload or refresh a source and its chunks re-embed, so agents answer from the current version of your docs.
File, document, or URL
One knowledge base entry, three ways to bring it in.
Upload a file and we chunk and embed its contents.
Bring structured documents your team already maintains.
Point at a page and ground answers in live web content.
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