Document intelligence that actually reads your business.
Drop your contracts, policies, and reference material in once — then your chat, your skills, and your customer-facing assistants answer from them, with citations you can click. No re-uploading. No copy-paste. And when the answer isn’t in your documents, it says so instead of guessing.
Retrieval-augmented generation, done properly.
“RAG” means the assistant retrieves the most relevant passages from your own documents before it answers — so responses are grounded in your material, not the model’s general training. Plenty of tools claim RAG. The difference is in the retrieval quality and the discipline around citations. This page walks through both — an accessible overview first, then the full technical pipeline for the evaluators who want to see the engineering.
Built to be trusted with the documents that matter.
Grounded and cited
Answers quote your documents with inline citations — and say “I couldn’t find that in your documents” instead of inventing an answer. No confident hallucinations.
Catches exact references
Vector-only search misses precise tokens like a statute or policy number. Our hybrid retrieval pairs meaning with exact keyword matching, so “IC 6-1.1” surfaces every time.
Isolated per workspace
Retrieval runs under the same database-level row security as the rest of the platform. Your documents can only ever answer your questions.
Reads real-world files
PDFs, Word docs, spreadsheets and text up to 100 MB — with page-accurate citations that open a PDF to the exact page.
How it works, end to end.
From the moment a document is uploaded to the moment a cited answer comes back, the system runs six stages. Each one exists to fix a specific way naive RAG goes wrong — chunks that cut sentences in half, vector search that misses exact numbers, or a model that confidently makes things up.
Six stages, one cited answer.
Citations you can verify, isolation you can trust.
Retrieval quality is only half the job. The other half is making the answer accountable — and keeping every workspace’s documents apart.
Cite or admit
The assistant is instructed to ground its answer in the retrieved passages, cite the ones it relies on, and say it couldn’t find the answer rather than invent one. The same discipline applies in chat, skills, and Document Search.
Open to the exact page
For PDFs, each chunk carries its page number, so a citation can open the source document at the precise page the claim came from.
Per-workspace by construction
Every retrieval runs inside your workspace’s database context, and the chunk store is governed by strict row-level security. Public assistants are further fenced off from internal admin and governance documents. See the isolation model →
The engine keeps getting sharper.
Document intelligence is an area we’re actively investing in — most recently with an attorney-grade retrieval upgrade.
On the roadmap
Roadmap items are in development and not yet generally available.
Let the assistant read your business.
Upload your documents once and get cited, grounded answers across chat, skills, and customer-facing assistants. Start with a free assessment.