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Mobility Insurance Co-Pilot

Motor insurers run separate models for crash prediction, claims fraud, and renewal. Each has its own pipeline, its own vendor, and its own quarterly refresh. This demo collapses all three into one shared model. Crash risk, claim integrity, and renewal retention come back together in under 50 milliseconds, computed from a single read of the policyholder's telematics and claims history, on the insurer's own infrastructure. Adding a new signal like UBI pricing or subrogation potential is a configuration change. Not a new model build.

Three signals from one read of the customer. Crash risk, claim integrity, and renewal retention come back together in under 50 milliseconds, on your own infrastructure. One model. One audit trail.
Sees what flat-feature models miss. Reads the full sequence of telematics events and claims, not just summary statistics. Picks up braking trajectories, night-driving trends, and provider-concentration patterns that hand-engineered features cannot represent.
New questions ship in days, not quarters. UBI pricing tier, subrogation potential, repair-fraud screen. Each becomes a specialized surface on the same shared model. No new pipeline. No new vendor.
Grounded analyst Q&A on the same model. Every prediction comes with a cited explanation, ready for the underwriting note. Free-text underwriter questions are on the roadmap.

The Problem

Motor insurers run five to fifteen separate ML models across pricing, claims, and retention. Each has its own pipeline and refresh cadence. Cross-task signals get lost because each model sees only its own features. A braking pattern that predicts both crash risk and renewal sensitivity is invisible to a model trained on one or the other. Cloud LLMs cannot ingest raw telematics streams at all.

How LFM Compares

Per-task GBDTs or actuarial tables on hand-engineered features. Each new business question means a new feature-engineering cycle, a new model, and a new deployment.

What LFM Unlocks

One model where today there are five to fifteen. Crash, fraud, renewal, and every new signal you add share the same foundation, the same deployment, and the same audit trail. The model reads raw telematics and claim events directly, so the feature-engineering step disappears. On-premise, so policyholder data never leaves your perimeter.

Policyholder historyLiquid Mobility modelCrash · Fraud · Renewal
Three underwriting signals from one read of the policyholder, in under 50 milliseconds.

Policyholders

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Select a policyholder to see crash risk, fraud, and renewal scores from a single model pass.

This demo is fine-tuned on sample data. Results improve with your data.

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