Claims paid faster. Fraud caught earlier. Audits answered with a query.

Insurance engineering sits at an unusual intersection: high-volume structured data, strict regulatory requirements, and business logic that changes faster than most teams document it. We've built claims automation that cut adjuster workload by 60%, underwriting pipelines that process 10,000+ applications per day, and fraud scoring models that flag suspicious patterns before claims are paid.

State DOI filings, NAIC data standards, ISO forms. Regulatory compliance in insurance is not a post-launch concern. We design systems with audit trails, versioned rule engines, and compliance reporting built into the core data model, so audits are a query, not a fire drill.

Actuarial models need clean, auditable data lineage. Underwriting decisions need explainability. Fraud models need recall tuned to the cost of false negatives. We build ML pipelines that produce outputs actuaries and compliance teams can defend, not black-box scores that create downstream liability.