Implement AI-Powered Process Automation for Your Agency

Most agencies rely on the built-in workflow tools within their Agency Management System (AMS). Systems like Applied Epic or Vertafore are great for rule-based routing, but they cannot interpret unstructured text. They can trigger a task if an email subject contains "New Claim," but they cannot read the email body to determine if it is a catastrophic fire or a minor cracked windshield.

This forces a human to be the bottleneck. Consider an agency with 5 producers that receives 40 FNOL emails a day. An office manager spends their morning reading each one, deciding its priority, and manually creating and assigning a task in HawkSoft. After a major hail storm, 150 emails arrive overnight. A critical commercial property claim gets buried for 6 hours, leading to a frustrated client and potential E&O exposure.

Some agencies try third-party document parsing tools, but these often fail on the variability of insurance claims. A generic parser can extract a policy number but misses the nuance between a "slow leak" and a "burst pipe." This lack of contextual understanding means the highest-risk claims still require manual review, defeating the purpose of the tool.

Syntora's approach to AI-powered claims triage for insurance agencies begins with a discovery phase to understand existing workflows and data. Syntora would start by identifying the primary First Notice of Loss (FNOL) sources, typically an Outlook 365 or Gmail inbox, and designing the API connection. Simultaneously, Syntora would analyze historical claims data, generally from the past 1-2 years within your Agency Management System (AMS), to map your specific severity levels and routing logic. This historical data is crucial for engineering an initial library of prompts for the Claude API, specifically tuned to the unique language and terminology used in your agency's claims documents. The client would provide access to these systems and historical data, along with subject matter expertise on claims handling.

The core of the system would be a FastAPI service, designed to monitor for new incoming emails at the designated FNOL source. Upon receipt of an FNOL report, the service would extract the email body text and any attached documents. This content would then be securely transmitted to the Claude API. Here, the AI would be tasked with classifying claim severity on a defined scale (e.g., 1-5), extracting key entities such as policy number, client name, and date of loss, and generating a concise summary, typically around 50 words. The technical goal for this processing, from email receipt to structured output, would be to complete within a sub-second timeframe.

This FastAPI service would be deployed using AWS Lambda for event-driven processing. This architecture scales automatically with demand and is designed to keep monthly hosting costs low, even with a high volume of claims. The processed and structured data (summary, severity score, extracted entities) would then be pushed into your existing AMS, such as Applied Epic, Vertafore, or HawkSoft, using its native API or webhook integration. A new task or record would be created and assigned to the appropriate adjuster, based on the AI-generated score and your agency's predefined routing rules.

For a complete audit trail, every AI decision, its confidence score, and the exact prompt used would be logged in a Supabase database. A critical safety gate would be built into the workflow: for any claim scored above a specified severity threshold (e.g., 4 out of 5), the system would require manual human review and approval before final assignment. This ensures high-stakes claims receive necessary human oversight. Syntora would work closely with your team during an initial tuning period, typically 2-4 weeks, to refine the accuracy of severity classification. Based on our experience building similar document processing pipelines using Claude API for financial documents, we would target a high accuracy rate, aiming for 95% or higher, for severity classification. Deliverables for such an engagement would include the deployed and tested system, source code, and documentation for ongoing maintenance and potential future enhancements.