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// Industry

Insurance AI development

AI for insurance — claims, underwriting, and document automation that are accurate, explainable, and auditable.

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// The problem

Why this is hard

Insurance decisions have to be right, explainable, and defensible — a claims or underwriting model that can't show why it decided what it did is a regulatory and reputational risk, not an efficiency win. The hard part is measurable accuracy, grounding in your policies, and an audit trail a regulator will accept.

// What matters here

The capabilities that move the needle

LLM Integration & Evals

Document extraction and decision support gated on an eval set, so accuracy is a number you can report and defend — not a claim.

RAG & Knowledge Systems

Answers grounded in your policies and filings with citations, so every determination traces to a real source.

AI Agents & Automation

Claims and back-office automation with scoped actions, validation, and human review on the edge cases.

MLOps & Infra

Traceability, reliability, and cost control for pipelines that run against sensitive data at volume.

// Proof

Shipped in production

Insurance — Claims triage

time to first action

-64%

median, vs. the shared-inbox baseline

adjuster capacity

3×

claims handled per adjuster-day

Read the case study

// FAQ

Common questions

Yes — inputs, retrieved sources, model version, and output are traced per decision, so a reviewer or regulator can reconstruct exactly why the system reached a determination.

We score every prompt and model change against a labelled eval set, so accuracy is measured and regression-checked, and low-confidence cases route to a human rather than an unattended decision.

We design for least-privilege access, auditability, and can run models under your data terms or in your environment, so sensitive data stays where your compliance team needs it.

// Related

All industries

// Let's build

Building AI for insurance?

Tell us where you are. We reply within a day with a concrete next step.