Retrieval systems that answer from your documents, not the open web

Most "AI search" fails for the same reason: it retrieves badly and then confabulates confidently. We build the retrieval layer first, evaluate it against questions you already know the answers to, and only then let a model write the sentence.

What is included

• Ingestion pipeline for PDFs, docs, wikis and codebases, with re-indexing on change.
• Retrieval tuned against a real question set, with measured recall before any answer is generated.
• Answers that cite the source passage, and refuse cleanly when the corpus does not contain it.
• An evaluation harness so you can prove quality did not regress when the corpus grows.

How the work runs

1. Map the workflowWe break down your process and identify what an agent can own end-to-end.
2. Design the agentModel selection, tool access, guardrails, on-chain permissions — designed with you.
3. Build & simulateAgents tested in sandbox simulations; contracts rehearsed on testnets first.
4. Deploy & monitorProduction launch with monitoring, alerting, and continuous improvement.

Skills