Generative AI Consulting for Insurance

FNOL capture, policy and billing Q&A, claim-status servicing, and intake of ACORD and photo/document packages. These are high volume, grounded in policy admin systems, and do not require payment authority. Settlement and coverage determination come later, if at all, under human checkpoints.

Not in a first production system, and not without a licensed human checkpoint even later. The model may retrieve and quote the applicable clause. Saying “this is covered” or issuing payment is an adjuster action with a logged approval.

Retrieve the insured’s in-force form and endorsements, require clause-level citations, refuse when retrieval is empty, and evaluate against real policy questions. A model that “knows insurance” without the actual form will still invent exclusions.

If the retriever returns no passage that answers the question, the system tells the user it cannot find the clause and escalates. It does not fill the gap from pretraining. That behavior is tested as a release gate.

On targeted servicing intents with core-system actions (status, billing, FNOL complete), 40–70% deflection is typical. Open-ended coverage debates and complex commercial claims sit at the low end or stay fully human.

One bounded servicing or FNOL workflow, with policy RAG and checkpoints, is a four-week implementation in your environment. Additional products reuse the form index and controls. A multi-line claims transformation is a program, not a sprint.

Yes. The agent reads policy and claim state through your existing APIs or services and writes FNOL and intake fields through the same channels, with every write gated and logged. We do not replace the policy admin system.

Each document is indexed with form number, edition date, and policy effective dating. Retrieval is filtered to the insured’s in-force set. Conflicting editions escalate rather than being merged into one answer.