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What we do·AI & Data·Generative AI

Generative AI Systems.

Copilots you can show a regulator.

We build generative AI that survives contact with compliance. Retrieval-grounded, citation-carrying, human-in-the-loop systems like Koda's claims copilot, used by 4,000+ insurance adjusters, and the eval-gated AI inside Ezra's FDA-cleared screening product. If the answer isn't in the source documents, our systems say so. On purpose.

96.4%
Gold-set pass rate in production
4,000+
Adjusters using the Koda copilot daily
−41%
Claim cycle time after launch

How we approach it

End-to-end delivery of RAG copilots, agents, and generative features, starting with use-case triage (two of Koda's six candidate features didn't need an LLM at all) and a gold-set eval harness built before the model is chosen.

End-to-end delivery of RAG copilots, agents, and generative features, starting with use-case triage (two of Koda's six candidate features didn't need an LLM at all) and a gold-set eval harness built before the model is chosen. Every output grounded, every claim cited, every inference dollar modelled before launch.

  • Use-case triage first. The features that don't need an LLM ship as cheap, hallucination-proof software
  • Gold-set eval harness before model selection; the model is whatever passes it
  • Retrieval-grounded outputs with citations back to source pages
  • Human-in-the-loop workflows where the stakes require a human
  • Inference-cost modelling before the prototype, not after the invoice
  • Audit trails fit for regulators. Every output logged with its retrieval set and approver

Proof

We've shipped this before.

FAQ

Before you ask.

Which model do you use?
Whichever passes the eval harness at the best cost. We're deliberately model-agnostic. The harness outlives every model choice, which is the point.
How do you stop hallucinations?
Grounding plus honesty. Answers must cite retrieved source passages, and when retrieval comes back empty the system says 'not in the file' instead of improvising. A fluent answer with a wrong citation fails our evals, no matter how good it sounds.
We're regulated. Is this even possible for us?
Regulated is our comfort zone. An FDA-cleared product and a fifty-state insurance copilot ship with our name on them. The audit trail isn't an add-on; it's the product.

How we approach it

End-to-end delivery of RAG copilots, agents, and generative features, starting with use-case triage (two of Koda's six candidate features didn't need an LLM at all) and a gold-set eval harness built before the model is chosen.

Every output grounded, every claim cited, every inference dollar modelled before launch.

Gold-set pass rate in production
96.4%
Adjusters using the Koda copilot daily
4,000+
Claim cycle time after launch
−41%

Proof

We've shipped this before.

FAQ

Before you ask.

Which model do you use?
Whichever passes the eval harness at the best cost. We're deliberately model-agnostic. The harness outlives every model choice, which is the point.
How do you stop hallucinations?
Grounding plus honesty. Answers must cite retrieved source passages, and when retrieval comes back empty the system says 'not in the file' instead of improvising. A fluent answer with a wrong citation fails our evals, no matter how good it sounds.
We're regulated. Is this even possible for us?
Regulated is our comfort zone. An FDA-cleared product and a fifty-state insurance copilot ship with our name on them. The audit trail isn't an add-on; it's the product.

Tell us the hard part.

A 30-minute call with an engineer, not a salesperson. Honest scoping, real dates.

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