About InsOps
The Problem We're Tackling
Generic AI Creates Risk for Insurers
Generic AI lacks the domain logic that governs insurance data, so it cannot interpret relationships, rules, or context accurately. This leads to outputs that are often incorrect or non-compliant.
Generic AI Is Not Trained on Insurance Data or Business Logic
General-purpose models lack understanding of insurance schemas, relationships, and rules, leading to incorrect interpretations and unreliable outputs.
Generic AI Cannot Follow Insurance Rules Reliably
Insurance operations depend on strict logic, validations, and calculations. Generic AI cannot consistently apply these rules in regulated, production workflows.
Generic AI Requires Moving Sensitive Data Outside the Environment
Most general-purpose AI models operate outside the insurer’s infrastructure, requiring PII and PHI to be transferred to external systems, which increases security and compliance risk.
Data Anonymization
Legacy systems retired, eliminating maintenance, duplication, and compliance overhead
Legacy Data Migration
Live claims data migrated and anonymized mid-cycle with no impact to adjudication or payouts
FAQ