A large specialty reinsurance firm managed 10,000 complex contracts, each worth $100M–$1B, covering sports stadiums, office buildings, and major infrastructure.
Contract language evolved constantly through negotiations and legal amendments, making it nearly impossible for humans to assess risk exposure consistently. Ad-hoc spot analysis took weeks of manual effort, leaving underwriting, claims, and balance sheet positioning exposed to significant uncertainty.
Mike Mayes' team built a GenAI prototype on a RAG-based architecture with semantic chunking, hybrid SQL/document store retrieval, and a custom reinsurance legal dictionary, enabling both structured data queries and open-ended risk questions across 10,000 complex contracts with 97%+ accuracy.
What previously required 2–3 weeks of manual analyst effort to complete ad-hoc contract risk spot analysis can now be done in less than a day by a single person, representing a dramatic acceleration in risk response time.
The system achieved and maintained 97%+ accuracy across 10,000 complex, 100+ page reinsurance contracts containing both highly specific structured data (dollar amounts, dates, regions) and ambiguous legal language around inclusions, exclusions, and cyber clauses.
The modular RAG architecture built for contract risk analysis is flexible enough to extend to claims data analysis and new contract negotiation support with minimal adjustments, providing compounding value from the initial investment.
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