Service 03
AI agents and answers
We build agents and search systems that answer questions over your own documents and data. The name for that is grounded RAG, short for retrieval-augmented generation. Every answer is read out of a retrieved source and traceable back to it. The system abstains when the data is not there.
What it includes
- One front door that sends each question to the agent that can answer it
- Search that finds the right source, and an answer cited to the exact row
- A question your data cannot answer gets an honest no, not a guess
What you get
- A working agent or ask-anything surface over your data
- A citation trail from every answer to the document behind it
- A search setup you can extend as your document collection grows
Proof
Where this ran
Delivered systems that used this service. Every figure carries the limit of what it measures.
A $3.4B US real-estate investment firm
Thirteen years of records. One box that answersRead the case1.000Correct every time it held backOne production system, at every release. It does not measure how often it should have refused.Ilayer research
AI that reviews code the way an adversary wouldRead the case7-gateEvery finding filtered before a human sees itOur own system, not a client's. The checks filter findings out; they do not prove one is exploitable.
Questions
What buyers ask about this
How do you stop the agent from hallucinating?
Every answer is read out of a real document and checked before it goes out. If a claim cannot be traced to a source, the system says it does not know.
How large a document collection can this handle?
We have run this over more than 29 million indexed chunks with cited retrieval. Chunks are the searchable passages a long document gets split into. The same setup extends 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 leaks of one client's data into another client's answers, checked in both of the places the system stores its search index.
