Leading Insurance Carrier Controls Agentic AI Costs and While Staying Compliant
A leading North American insurance carrier is scaling agentic AI across claims, legal, and procurement, all centered on unstructured document processing at volume. As deployments expanded, costs grew far beyond early projections and emerging NAIC, federal, and state requirements demanded continuous compliance evidence across every production deployment. The carrier needed a platform that could address both problems without adding to an already strained cost base.
With Fiddler, the carrier deployed an agentic Virtual Claims Assistant (VCA) and a Demand Letter Summarizer (DLS), and extended observability across legal and procurement workflows. Fiddler Centor Models run in-environment, delivering model quality scoring, PII detection, and quality monitoring with no external API calls and no additional cost.
Delivering Visibility, Context, and Control with the AI Control Plane
With Fiddler, the insurance carrier achieved:
- Token usage and cost visibility without evaluation overhead: visibility into model quality, usage, and PII exposure across document workflows with no external API costs.
- Faster processing of document-heavy claims work: extracted key entities from long-form documents, including parties involved, amounts, and medical details, reducing manual review time.
- Unified governance across the AI portfolio: centralized visibility across agentic and ML workflows through executive dashboards.
- Audit evidence for compliance: governance requirements mapped to trackable metrics, monitored continuously in production.
Operationalizing Responsible AI in a High-Stakes Environment
The carrier faced three interlocking challenges.
- AI costs had grown well beyond original projections across legal (depositions), claims (demand packets), and procurement (contracts). Despite competitive infrastructure pricing, the gap between forecast and actual spend was significant. With users actively depending on these capabilities, reducing scope was not an option.
- The carrier had no tooling to understand what was driving costs or how to optimize affordably. Monitoring usage, detecting PII, and evaluating model quality with external LLM-as-a-judge tools would only add to the bill.
- Governance requirements were easy to state and difficult to operationalize. Teams needed to translate policies into continuously measurable signals and generate evidence automatically, not through periodic manual reporting. The shift to agentic workflows introduced new production risks that traditional ML monitoring could not address.
Establishing the AI Control Plane for Insurance Operations
Fiddler Centor Models run entirely within the carrier's environment, scoring model outputs, detecting PII, and monitoring quality signals with no external API calls.
The carrier can now evaluate model quality across all three business lines without incurring the Evaluation Trust Tax, gaining visibility into what is driving AI spend for the first time.
Fiddler also gives the carrier centralized oversight to govern all their AI applications and meet compliance requirements. These requirements are mapped to trackable metrics monitored continuously in production, while unified monitoring across agentic and predictive AI applications enables root cause analysis to pinpoint what changed, where, and why, generating the audit evidence NAIC, federal, and state regulators require.
Scaling AI Operations Without Sacrificing Cost Control or Compliance
With Fiddler, this leading insurer moved from cautious experimentation to controlled, production-grade adoption of agentic AI across claims, legal, and procurement.
The carrier can now manage AI costs, maintain governance visibility, and provide compliance audit evidence across its full document processing operation. With license expansion underway, the carrier continues to invest in Fiddler as the foundation of its enterprise AI governance strategy.