Clause-Level PFAS Detection at Scale: Deploying Governed GenAI in a Regulated Insurance Environment

As regulatory scrutiny around PFAS (Per- and Polyfluoroalkyl Substances) intensified, a leading insurance carrier faced a growing challenge: understanding how PFAS-related clauses, exclusions, and exposures were embedded across a vast portfolio of policy documents. With over 100,000 unstructured documents, the organization needed to identify relevant policies, extract critical clause-level information, and build reliable visibility into PFAS exposure.

Client Challenges and Requirements

• Manual review was no longer viable — reviewing tens of thousands of documents by hand was inefficient, costly, and unable to meet regulatory timelines.
• Accuracy and validation were non-negotiable — PFAS-related determinations directly impacted risk assessment and regulatory compliance.
• AI adoption was constrained by governance and security requirements — model usage had to respect enterprise data controls, auditability, and traceability.

Bitwise Solution

• Designed an end-to-end, multi-stage LLM-orchestrated pipeline separating document identification, text extraction, clause detection, metadata extraction, and validation into distinct, controllable stages.
• Combined OCR, digital PDF parsing, and document readers to produce clean, page-wise text with structured data flows across bronze, silver, and gold layers.
• Enforced accuracy through a robust validation framework including manual ground truth comparisons, field-level accuracy metrics, and confusion matrices.
• Designed architecture to process documents in parallel while maintaining governance controls and data security.