Retrieval, grounding, citations, evaluation, and orchestration — the engineering that turns a model into a production assistant that knows your data, shows its sources, and knows when to escalate.
Retrieval-augmented generation grounds every answer in your own content — so responses are current, specific, and cite where they came from.
RAG (retrieval-augmented generation) is how we stop AI from hallucinating. Before the model answers, it retrieves the most relevant passages from your knowledge base, grounds its response in that evidence, and returns citations the user can verify. Agents take this further: they plan, call tools, retrieve, and act in a loop — with guardrails so they escalate to a human when confidence is low. This is the layer most teams skip, and it is the difference between a demo and a product. Every one of our 130 production agents is built retrieve-before-act, and captures what it learns back into its knowledge base.
Five disciplines that turn a raw model into a trustworthy production assistant.
We tune retrieval to your data — chunking, hybrid search, and re-ranking — so the model always sees the most relevant evidence before it writes a word.
Production agents plan, retrieve, and call tools in a loop — but with circuit breakers, spend caps, and human escalation so they never run away or answer beyond their confidence.
The real, relevant stack we build this with — model-agnostic, open-source-first, production-grade.
Every technology below is delivered by the same composed engineering team.
We’ll build the retrieval, grounding, and evaluation layer that turns a model into a product you can trust.
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