Service 03
AI agents and grounded RAG
We build agents and retrieval systems that answer questions over your own documents and data. The difference is grounding: every answer is read from a real record and traceable to its source, and the system abstains when the data is not there rather than fabricate a figure. That approach has a name, grounded RAG: retrieve from your records first, then answer only from what was found.
What it includes
- An orchestrator that routes each question to the right specialist agent
- Hybrid retrieval with a reranking stage, and cited answers down to the record
- An abstain path, so an ungrounded question gets an honest no-answer
What you get
- A working agent or ask-anything surface over your data
- A citation trail from every answer to its source document or row
- A retrieval setup you can extend as your corpus grows
Common questions
How do you stop the agent from hallucinating?
Answers are grounded in retrieved records and checked by a multi-stage verifier before they are served. When a claim cannot be traced to a source, the system abstains.
How large a corpus can this handle?
We have run this over more than 29 million indexed chunks with cited retrieval. The same setup extends to a larger corpus without a rebuild.
Can it keep one client's data out of another's answers?
Yes, and we measure it. On one production system we hold zero cross-tenant leaks across both embedding spaces, verified by the eval gate.
Start a project
Name the workflow that eats your team's week
Name the workflow that eats your team's time. We will tell you honestly whether AI fits, and if it does, scope a first piece with a known price.