RAG as a Service
Trusted answers from your enterprise data — retrieval, grounding and citations, ready for real work.
- <2s median answer time
- 96% citation accuracy
- 40+ source connectors
Answers your team can act on, with the receipts attached
Connect your sources
Documents, wikis, ticket systems and databases indexed behind a single endpoint.
Citations you can verify
Every answer points at the passage it came from, so nothing has to be taken on trust.
Evaluation harness
Answer quality is measured against a graded set, not assumed after a good demo.
Permission-aware
Retrieval respects the access rules of the source system, per user.
Freshness controls
Re-indexing runs on a schedule or webhook, so answers track the source.
Drop-in API
One endpoint to query; bring your own front end or use ours.
Answers anchored to your own documents
Queries are answered from your indexed corpus rather than the model memory. Where the corpus does not support an answer, the system says so instead of inventing one.
- Hybrid keyword and semantic retrieval
- Abstains instead of guessing
- Tunable recall per collection
Every claim points at its source
Answers carry inline citations back to the exact passage. Reviewers settle disagreements by opening the source, not by arguing with the model.
- Passage-level, not document-level
- Exportable for audit
Quality measured, not hoped for
A graded question set runs on every index change, so regressions are caught before users meet them. You get the numbers, not a vibe.
Tech stack & integrations
Not sure where AI actually fits your business?
Answer a few quick questions. A senior engineer reads them personally and comes back with a concrete first step. Book a call at the end if it is a fit.
Find out in 20 secondsFAQs
What is retrieval-augmented generation?
The model answers from documents retrieved out of your own corpus at query time, rather than from what it memorised in training. That is what makes the answer checkable.
Which sources can you index?
Documents, wikis, ticket systems, databases and object storage. Confluence, SharePoint, Google Drive, Notion and S3 ship as connectors; anything else goes through the API.
How do you stop it making things up?
Answers are grounded in retrieved passages and carry citations. Where retrieval does not support an answer, the system abstains instead of guessing.
Does it respect our permissions?
Yes. Retrieval is permission-aware per user, honouring the access rules of the source system, so nobody sees content they could not open directly.
How do we know the quality is good enough?
You get an evaluation harness with a graded question set that runs on every index change, so regressions surface before your users meet them.
How quickly can we go live?
A four-week pilot: one week to connect and index, two to tune retrieval and evaluate, one to wire it into your front end.