Answers from your documents, with the source.
What we build.
Document Q&A and search
Your team asks; every answer cites the page it came from.
Support and knowledge assistants
Grounded answers inside the tools your team already uses.
Retrieval inside your product
Search and answers as a feature your own users see.
Evals for an existing system
Find out how often your RAG system is right, and fix what is not.
How an engagement runs.
A golden set and a success measure agreed first.
Ingestion, retrieval and evals, shown every week.
Production rollout with monitoring switched on.
Accuracy and drift watched after launch.
A typical engagement.
- Duration
- [6–10 weeks]
- Team
- [2–3 engineers]
- You get
- The system, its eval set and a runbook
- Starts with
- An AI Audit or a scoping call
How we measure it.
A golden set
Real questions from your team, each paired with the passage that answers it.
A retrieval score
Did the system find the right passage? Scored on every change.
An answer score
Did the answer use that passage correctly? Scored separately, so we know which half to fix.
What teams ask about RAG.
Can it use our existing documents and permissions?
Yes. We connect to where the documents live and respect who is allowed to see what.
Which models and vector databases do you use?
Whatever fits your data and hosting rules. We recommend during the audit, with the reasoning.
Where is our data stored?
[In your cloud account by default. We never train models on your data.]