Risk appears
Claims, churn, and morbidity signals surface after the useful contact window.
Request accessInsurance prevention intelligence
Act before portfolio risk becomes claims.
Ensembling helps insurers turn late-moving risk signals into simulated futures, ranked prevention actions, and clearer executive decisions.
Twins simulate
Synthetic policyholder trajectories test what is likely to happen next.
Teams act
Prevention, underwriting, and portfolio moves become ranked decisions.
Decision cockpit
Portfolio prevention run
Live scenario
Risk trajectory
Baseline vs prevention scenario
-14%expected loss
SignalDetected
TwinSimulated
ActionRanked
Decision queue
01
High leverage cohort
8.4k members02
Broker concentration
2 regions03
Renewal risk drift
+6.8%ST
Synthetic twin cohort
Prevention leverage: 82/100
Baseline42%
Prevention68%
Combined lift81%
Problem to solution
A clearer story for the decision-maker: see the window, test the move, act earlier.
The product is built around one commercial question: which policyholders or portfolio segments deserve action before the claim pattern becomes obvious?
01
Chapter 01
The insurer sees the pattern too late.
Teams can measure losses with precision, but the intervention window is often gone by the time the dashboard turns red.
Problem: reactive risk management02
Chapter 02
A synthetic twin makes the next move testable.
Ensembling builds probabilistic versions of policyholders and segments, then compares possible future paths before rollout.
Shift: scenario-based prevention03
Chapter 03
The business gets a ranked action, not another report.
Underwriting, claims, prevention, and portfolio teams see who to review, when to intervene, and what assumption drives the recommendation.
Result: earlier, explainable actionDecision cockpit
Portfolio prevention run
Live scenario
Risk trajectory
Baseline vs prevention scenario
-14%expected loss
SignalDetected
TwinSimulated
ActionRanked
Decision queue
01
High leverage cohort
8.4k members02
Broker concentration
2 regions03
Renewal risk drift
+6.8%ST
Synthetic twin cohort
Prevention leverage: 82/100
Baseline42%
Prevention68%
Combined lift81%
Platform intelligence
A compact cockpit for synthetic twin decisions.
The platform combines trajectory views, synthetic cohort profiles, scenario lift, and recommended actions in one insurer-side workflow.
Portfolio drift
Prevention lift
Underwriting review
Executive readout
Operating model
Designed for insurer-owned data, teams, and channels.
01
Connect governed data
Policy, claims, health, channel, and portfolio signals stay inside insurer-owned workflows.
02
Create synthetic trajectories
Segments become simulated futures with confidence, drift, and prevention leverage attached.
03
Compare scenarios
Teams test baseline, outreach, underwriting, monitoring, and cost assumptions side by side.
04
Operationalize the next best move
Ranked actions flow into dashboards, APIs, reports, or white-label decision surfaces.
Team
A focused team for regulated AI products.
The team combines data science, mathematics, software engineering, legal governance, and go-to-market work across academic and industry environments.
Lorenzo Buccolini
CEO
Data Science at University of Pisa and TUM. Research experience across financial services and entrepreneurship.
Data scienceProductResearch
Pietro Grassi
CTO
Data Science Honors at Scuola Superiore Sant'Anna, with a mathematics and computational modeling background.
ModelingHealth dataArchitecture
Andrea Di Matteo
Head of Software Engineering
Computer Engineering Honors at Sant'Anna. Software engineering experience across platform, systems, and AI products.
SoftwareSystemsDelivery
Serena Pauciulo
Head of Marketing
Marketing at King's College London, with international and early-stage brand experience.
MarketingBrandResearch
Edoardo Bugliani
Head of Legal
Law Honors student focused on data governance, regulated markets, and operational legal work.
LegalGovernanceOperations
Private briefing
Send the brief to the decision-makers evaluating prevention intelligence.
The brief explains the operating problem, the product direction, and the insurer-side workflows for proactive risk decisions.