Insurance runs on decisions, not processes

Solutions

Common questions

What operators ask before the first call

How long before an AI agent in claims intake pays for itself?

Most carriers see payback inside two to three renewal cycles on FNOL and document intake alone, because the cost isn't the agent — it's the adjuster hours spent re-keying and chasing missing documents. The faster path is picking one high-volume, low-complexity claim type first: auto glass, small property, or simple liability. Payback timing depends more on claim mix and current touch time than on the technology, which is why we cost it against your actual volume before committing to a number.

What data do we need before we can start?

You need read access to your claims or policy admin system, a sample of historical claims with outcomes, and whatever intake channels you currently support — email, portal, fax, or voice. You don't need clean data or a data warehouse; agents can work against the messy, real version of your systems. What matters more is having someone who can define what 'severe' or 'clean' means in your shop, because that judgment has to be encoded before an agent can apply it consistently.

How do we keep an agent auditable for regulators?

Every recommendation the agent makes — route this claim to a senior adjuster, flag this submission as incomplete — needs a reasoning trace: what data it saw, what rule or pattern triggered the output, and what a human did with it. We build that logging in from day one rather than bolting it on later, because retrofitting audit trails after a state market conduct exam request is far more expensive than building them upfront.

Will this replace adjusters or underwriters?

No — the economics don't work that way. Adjusters and underwriters are constrained by triage and assembly time, not by judgment capacity. Agents take the sorting, document review, and status-chasing off their desks so the hours that remain go to the claims and submissions that actually need a human decision. Headcount plans should assume redeployment toward complex work, not reduction, at least in the first several deployments.

What's the difference between this and the chatbot our carrier already tried?

A chatbot answers questions; these agents make and act on decisions inside your policy admin or claims system — triaging a submission, clearing a stipulation, updating a claim status. The distinction matters for ROI: a chatbot reduces call volume, but a decision agent reduces cycle time and touches the metrics your board actually tracks, like loss adjustment expense and time to first contact.

Perspectives