Reduce Manual Underwriting Time With Custom AI

Independent insurance agencies run on their Agency Management System (AMS) like Applied Epic, Vertafore, or HawkSoft. These platforms are excellent systems of record for managing policies and client data. They are not, however, built to perform the cognitive work of underwriting. An AMS can store a loss run PDF, but it cannot read the document and identify a pattern of increasing claim frequency.

Consider a typical workflow for a new commercial property submission. An underwriter receives an email with a 15-page PDF containing ACORD forms and five years of loss history. They must first manually read the entire package to find and re-key dozens of data points into the AMS. Then, the real work begins: checking the property address on Google Maps for hazards, looking up business details, and cross-referencing against internal guidelines. This is 45-60 minutes of low-value data extraction per submission before any expert analysis happens.

The structural problem is that an AMS is a database with a fixed schema. It is architected to handle structured data entered by humans, not to interpret unstructured documents or make API calls to external services. You cannot add a feature to your AMS that automatically flags submissions from businesses in high-risk flood zones because the platform's core design lacks the hooks for external data integration and probabilistic analysis. This forces your most experienced people to spend their time on clerical work.

Our process would begin with an audit of your current underwriting workflow for a single line of business. Syntora would review your submission documents, underwriting guidelines, and the external data sources you consult. The goal is to create a detailed map of your decision-making process, which becomes the blueprint for the AI system. You receive a scope document outlining exactly what the system will check and how it will report its findings.

The core of the system would be a FastAPI service running on AWS Lambda, ensuring low operational cost (likely under $50/month at moderate volume). When a submission email arrives, the Claude API parses the attached ACORD forms and loss runs to extract key data points. Python scripts then use this structured data to query external APIs in parallel. Pydantic models enforce strict data validation, ensuring that malformed data from a carrier PDF does not corrupt the final output. We've used this exact document processing pattern to parse complex financial reports; it applies directly to insurance forms.

The delivered system would integrate with your existing AMS. For each submission, it would post a structured summary of its findings, a preliminary risk score from 1-100, and a list of specific red flags for review. The entire process from receiving an email to seeing the summary in your AMS would take under 120 seconds. Your underwriters would start their work with a pre-analyzed package, not a blank slate.