Insurance work fails when a model is accurate in a test set and silent about why it scored a particular claim, quote, or customer. Reviewers, complaints teams, and model-risk groups need the inputs, the threshold, and the path a case took, not only a label.

What makes AI work different here

Where we typically start

We start by naming the decision the system is allowed to make, what must stay with a person, and which documents every answer has to cite. Classification, triage, and a model-risk review are different projects, and we do not treat them as one.

Services we typically provide

Case studies from this industry

Frequently asked questions

Can a model deny a claim on its own?

We do not recommend a fully automatic denial. The usual pattern is a model that classifies and gathers the file, with a person making the decision that changes what the customer is paid.

How do you keep a coverage answer tied to the policy?

The assistant is limited to retrieved passages from the policy, endorsements, and claim notes. If the source is missing, it says so and hands the question to a person.

What does a model-risk review need from the build?

A record of training data, the decision threshold, where the score is used, and how a reviewer can see the inputs. We build that in at the start so the review has something concrete to read.