Insurance & InsurTech

For minor to moderate exterior damage, our custom Computer Vision models (trained on millions of historical accident photos) can identify part damage, assess severity, and cross-reference OEM parts catalogs to estimate repair costs with 85-92% accuracy compared to a human adjuster. This allows insurers to offer 'straight-through processing' (instant payouts) for low-risk claims, reducing operational overhead by up to $150 per claim.

We utilize the 'Strangler Fig' pattern. Instead of attempting a risky multi-year rip-and-replace of an AS/400 mainframe, we build a modern API gateway and microservices layer in front of it. We sync data from the mainframe to a high-speed read replica database (like PostgreSQL) using Change Data Capture (CDC). The mobile app queries the fast, modern database, providing a sub-second user experience while the legacy system safely remains the system of record.

Regulatory compliance demands that algorithmic underwriting is fair and explainable. We implement rigorous MLOps pipelines that track 'model drift' and automatically run fairness metrics (like Disparate Impact Analysis) against protected classes. Furthermore, we utilize Explainable AI (XAI) frameworks like SHAP to generate human-readable reasoning for every automated rejection, ensuring full compliance with state Department of Insurance (DOI) audits.