Insurance.

Insurers spend more on document handling than on the claim decisions themselves. Submission packets, medical reports, and adjuster notes are where the unit-economics live, and where generative AI moves the curve. In practice, the first deployments cluster around claims summarisation, document intelligence, structured extraction, evaluation harnesses: areas where evaluation criteria are objective, data is already in the system, and an evaluation harness can measure quality continuously.

Regulators-of-note for insurance engagements typically include APRA, NAIC, PRA, FINMA. The specific regulators that bind a given engagement depend on jurisdiction and the data classes in scope, so we map this explicitly during discovery rather than assume a global posture.

We do not deploy generative AI to make final claim-acceptance or coverage decisions. Outputs feed adjuster queues with structured handoffs and audit trails. This is a deliberate trust boundary, not a capability gap. We are equipped to build the systems we decline to build, and we decline to build them because the risk-to-value ratio in this industry does not justify it.

Our insurance engagements typically sit between Chief Claims Officer, Head of Underwriting, Director of Operations and equivalent senior operators. The decision-maker varies by organisation, but the common thread is a leader accountable for both delivery and downside.

In insurance, the strongest generative AI use cases are claims summarisation, underwriting research, and document classification across submission packets. The same techniques look superficially similar across industries, but the data shapes, evaluation criteria, and adverse-event posture differ enough that copy-pasting an engagement from another industry usually produces a system that fails the first audit it sees.

We spend two weeks with your leadership and operators, rank the use cases that are worth doing, and come back with a clear plan and a clear price. We never start with a six-month strategy exercise. We go after the smallest piece that produces real value first.