Calculate Your Firm's AI Claims Processing ROI

Most small insurance firms run on an AMS like Applied Epic, Vertafore, or HawkSoft. These platforms are excellent systems of record but are not built for intelligent workflow automation. Their built-in tools for claims intake are often rigid, forcing manual workarounds for anything outside a standard data entry form. The core function is storage, not processing.

Consider a typical scenario: an FNOL report arrives as a PDF attachment in an email. An adjuster or admin must open the email, download and read the PDF, identify the policy number, and then manually search for the client in Vertafore. They create a new claim record, copy-pasting details from the PDF. They then skim the document for keywords like 'injury' or 'total loss' to gauge severity and decide which adjuster to assign it to. This entire sequence takes 15-20 minutes of skilled adjuster time for a single claim.

The structural problem is that an AMS is not designed to interpret unstructured data. It cannot read a 10-page police report and extract the relevant names, dates, and incident summaries. Off-the-shelf automation platforms also fail here; they can connect applications but lack the sophisticated document parsing capabilities of a true AI model. They cannot handle the sheer variability of FNOL documents, from a client's brief email to a multi-page legal notice.

The first step is a technical audit of your existing claims process. Syntora would start by reviewing 50-100 of your recent FNOL reports, mapping every intake channel and document format. We would work with your team to codify the exact logic they use to assess severity and route claims. This discovery phase produces a detailed architectural plan that you approve before any development begins.

The core of the system would be a series of AWS Lambda functions connected to your intake channels. When a new document arrives, a Lambda function sends its content to the Claude API, which is specifically trained for extracting structured data from unstructured text. The parsed data, including a generated summary and a severity score, is then passed to a FastAPI service. This service uses your firm's custom logic to determine the correct adjuster and prepares the data for your AMS.

The final system integrates directly with your AMS. It creates a new claim record in Applied Epic or HawkSoft, populates it with the extracted data, attaches the original documents, and assigns the claim to the right adjuster's queue. Your team continues to work inside their familiar AMS, but the first 20 minutes of manual triage work for every new claim is completely eliminated. The system costs less than $50 per month to run on AWS for typical claim volumes.