Industries · Insurance

AI Decision Intelligence
for insurance companies

Improve claims, renewals, fraud detection, pricing, agent productivity, and customer experience, on data you already collect.

2.1d
Claims turnaround with AI triage, down from 5.4d
41%
Routine claims cleared straight-through
+$5.7M
LTV improvement from decisioning (founder track record)
<1%
False-positive target on prioritized fraud flags
The challenges

The decisions that quietly drain margin

Insurers sit on rich claims, policy, and billing data, yet the highest-value calls are still made late, manually, and inconsistently.

! Claims delays and inconsistent triage
! Policy lapses caught too late to save
! Fraud that slips past manual review
! Pricing pressure eroding margin
! Uneven agent productivity
! Customer retention treated reactively
! Manual operations that don’t scale
AI use cases for insurers

Where the decision engine pays off

Claims Automation

Auto-clear routine claims; route complex ones.

Renewal Prediction

Flag lapse risk before the renewal window.

Retention Recommendations

Best intervention per policyholder.

Fraud Detection

Score and prioritize suspicious claims.

Claims Severity Prediction

Estimate exposure early for reserving.

Pricing Optimization

Customer-level price sensitivity.

Agent Performance Intelligence

Prioritize workload and coaching.

Next Best Product

Recommend the right cross-sell.

Recommended first project

Start where the ROI is clearest

Lowest risk

AI Opportunity Roadmap

A three-week sprint that prioritizes and quantifies your highest-value AI use cases before any build.

Explore the Roadmap →
Fastest payback

Renewal Intelligence Pilot

Predict lapse, estimate LTV, and recommend the best save on a single book of business.

See Solutions →
Common questions

What insurers ask before starting

Do we need clean, unified data first?

No. We start with the claims, policy, and billing data you already have, in whatever shape it's in. The Opportunity Roadmap surfaces which gaps actually matter for the decision at hand, so you invest in data quality where it changes an outcome, not everywhere at once.

How is this different from the analytics we already run?

Dashboards tell you what happened. The decision engine chooses the next action for a specific claim, policy, or policyholder, then measures whether that action produced incremental lift against a holdout. It closes the loop between a prediction and the money.

Will it respect our regulatory and fair-treatment obligations?

Constraints are built into the recommendation, not bolted on after. Every action is auditable, and reserving, solvency, and fair-treatment rules are encoded as guardrails the engine has to satisfy before it recommends anything.

How long before we see measurable impact?

A first pilot, typically on renewals or claims triage, runs on a single book of business and reports lift against a holdout within the quarter. You see a real number tied to real cases before committing to a broader rollout.

Let's discuss AI for your insurance business

Bring your biggest claims, renewal, or fraud challenge, we'll map the highest-value place to start.

Book a call →