AI Agents

Context

Built for insurance within the Renewals & Upsell stack and used by Policy Admin / Pricing teams, this agent focuses on a specific gap: turning raw telematics and IoT usage data into signals that pricing and customer-facing teams can actually use. It is designed for Tier 2–3 insurers running usage-based or telematics-enriched products where driving or usage patterns should influence price and conversations, but today sit as unread sensor feeds.

What it does

The agent ingests telematics and IoT streams for insured assets—driving behaviour, mileage, time-of-day usage, locations within accepted geofences, simple event flags like harsh braking or speeding—and consolidates them into clear usage profiles and risk-relevant metrics per policy. It maps these patterns onto your existing rating or risk tiers so underwriters and pricing teams can see which behaviours should move a customer into a higher or lower risk band, and it produces simple usage summaries that can be used in customer communications or nudges (“you drove fewer night miles this period”, “hard braking events decreased”). The result is that telematics data becomes a structured input to pricing and engagement, not just a raw feed.

Core AI functions

The core capability is time-series and sensor analytics. The agent cleans and aggregates sensor data over time, detects stable patterns versus short-term noise, and transforms them into a small set of risk and behaviour indicators aligned with your pricing logic (for example, usage intensity, driving smoothness, risky time bands). These indicators can then be consumed by rating engines and by downstream agents that need a simple, stable representation of “how this asset is actually used”.

Problem solved

Raw telematics on its own is not actionable: pricing teams see event logs and charts rather than clear “better/worse risk” signals, and customer-facing teams struggle to explain how usage will affect premiums. As a result, telematics programs either stay underused in pricing or turn into black boxes where customers don’t understand what behaviour matters. This agent closes that gap by interpreting sensor data into risk-linked usage metrics and simple narratives that can feed pricing, renewal, and engagement flows.

Business impact

The direct impact is more accurate pricing and safer behaviour nudges. By grounding premiums and risk tiers in actual usage patterns instead of static assumptions, the insurer can align price more closely with risk and reward safer patterns over time. At the same time, exposing interpretable usage summaries enables clearer conversations with customers about how their behaviour affects price, supporting programs that encourage safer driving and more transparent, data-backed renewals.

Integration and adjacent use cases

Integration complexity is medium: the agent needs access to telematics/IoT data feeds, policy references, and your rating or risk-tier structures, and it writes its interpreted usage metrics and summaries back into policy admin, pricing tools, and customer-facing systems.

Common combinations in this stack:

• Lapse Risk Predictor to factor usage-based satisfaction and behaviour into lapse propensity scores;

• Premium Change Impact Explainer to include usage-based drivers (for example, improved or deteriorated driving patterns) in the “why your premium changed” breakdown;

• Product Suitability Re-Matcher to suggest better-fitting products or cover structures when actual usage diverges from original assumptions; and

• Cross-Sell / Upsell Trigger Agent to propose relevant add-ons or upgrades at renewal based on how the customer actually uses the insured asset rather than just static profile data.