Better risk selection. Smarter next actions. Profitable growth.
Underwriting, pricing, retention and growth decisions powered by models trained on your policy data, customer behaviour and insurance-specific signals - true 1:1, not segmentation.
Outcomes, not slideware.
The right product, customer, moment.
A reinforcement-learning Next-Best-Action engine that picks across your portfolio - not a static rule chart.
Churn predicted before it shows up in a renewal report.
Policy, claims and behavioural signals combined into a model that knows your book.
Risk selection guided by your loss history.
Models trained on your portfolio, not generic industry benchmarks - with explainability for every score.
Granular, defensible, regulator-ready.
Rate adequacy and elasticity insight where it matters; transparent factors that survive audit.
Discover. Pilot. Production. Compound.
Discover
One decision - cross-sell, retention or risk selection - with the data we'll use to prove it.
Pilot
Champion–challenger against your current logic, on a real cohort.
Production
Model wired into the channel that acts on it - CRM, policy admin or contact centre.
Compound
Feature stores, label pipelines and explainability tooling carry into the next decision.
Workflows we have taken to production.
Next-Best-Action
Across motor, health, life and SME, on the channel the customer prefers.
Churn prediction
Renewal probability scored on policy, payment and engagement signals.
Underwriting scoring
Risk scores trained on your portfolio, combining ACORD forms, loss runs and external signals with factor-level transparency.
Loss-ratio improvement
Sub-portfolio identification and pricing actions to close the gap.
Broker / agent intelligence
Performance and quality of book by intermediary, predictive on conversion.
Lifetime value modelling
Acquisition decisions priced against expected LTV, not first-year premium.
Senior, embedded, accountable.
- A senior actuarial or pricing lead.
- An ML engineer who has shipped models into core insurance systems.
- A data engineer who understands policy-admin and claims schemas.
- Hand-off to your data science team with documentation, MLOps and runbooks.
Repeatable work, taken off the team.
- Feature pipelines trained on policy, claims, payments and engagement data.
- Champion–challenger evaluation agents that score model drift and lift.
- Explainability layer producing factor-level reasoning per decision.
- Action-routing agents that put the recommendation in your CRM, IVR or app.
Tell us the workflow that's costing you the most.
In time. In headcount. In customer experience. We'll tell you whether we can move it in 6–8 weeks - or we'll tell you we can't.