The study analyzed 346 real-world loss runs to compare Bevaya’s insurance-trained technology against leading general-purpose AI. While high-end general models consistently plateaued between 78% and 85% accuracy, the specialized model outperformed them by a significant margin. This discrepancy is particularly critical for identifiers like claim and policy numbers, where Bevaya’s model achieved 86.6% accuracy compared to 72.8% for its general-purpose counterparts.

Beyond raw field accuracy, the specialized approach addresses the operational bottleneck of manual verification. Because insurance workflows require complete data integrity, a single error often necessitates human intervention. Bevaya reported that its model required less than half the processing time of general alternatives and produced nearly twice as many error-free documents, allowing for higher straight-through processing rates. According to CEO Chaz Perera, the performance stems from training on hundreds of millions of non-public documents labeled by insurance practitioners, rather than relying on public datasets that lack industry-specific logic.

To bridge the gap between benchmark performance and the 98%+ accuracy required for production, Bevaya employs a secondary verification layer. This system cross-references outputs against source documents and provides confidence scores, routing uncertain items to human staff via a patented human-in-the-loop interface. By documenting every decision, the company aims to meet increasing regulatory demands for transparency in automated insurance underwriting and claims processing.