Automate Claims Data Extraction in 3 Months

Most 15-person claims teams rely on their Agency Management System, like Applied Epic or Vertafore, for core operations. These platforms have workflow features that can flag an incoming email with 'FNOL' in the subject line. The automation, however, stops at the document's edge. An adjuster must still open the attached PDF, read it, and manually key 20 to 30 fields into the AMS to create a new claim. This process is slow and a major source of data entry errors.

Some firms attempt to use generic OCR tools to solve this. An OCR tool extracts raw text but fails to understand context. It might pull 'Policy Number: 12345' as three separate strings ('Policy', 'Number:', '12345') and cannot differentiate the 'Date of Loss' from the 'Date of Report'. This forces an adjuster to clean up the messy output, which often takes more time than just typing the data from scratch. The tool creates more work than it saves.

In practice, this means a senior adjuster spends 15 minutes transcribing a standard FNOL report. When a non-standard, multi-page document arrives from a new broker, that time can double. For a team handling 50 new claims a day, this is over 12 hours of skilled labor spent on low-value data entry. The risk of a mistyped policy number or incorrect date of loss introduces errors that impact liability and client trust.

The structural problem is that an AMS is a system of record, not an intelligence engine. Its architecture is built for structured data entry, not for interpreting the unstructured content of a PDF or email. These systems lack a semantic understanding layer, which is why a human must always act as the bridge between the source document and the database.

The project would begin with a 2-week discovery audit. Syntora would work with your claims team to gather 50-100 de-identified examples of every document type you process: FNOLs, ACORD forms, and supplemental reports. We would map every data field for extraction and codify every validation rule your adjusters currently apply manually, such as checking if the loss date is within the active policy period.

The technical system would be an AWS Lambda function that processes new documents. This function uses the Claude API to read each document, identify entities, and structure the output as JSON. We propose the Claude API for its strong performance on varied document layouts and scanned images. A separate FastAPI service would handle the validation logic, connecting to your AMS via its API (e.g., the Applied Epic SDK) to verify policy numbers and coverage details. All processing logs and exceptions would be stored in a Supabase database for auditing.

The delivered system integrates directly into your existing workflow. Documents sent to a specific email address would be automatically processed. Validated claims data populates a new record in your AMS, with a task assigned to the appropriate adjuster. Any document that fails validation is routed to a simple exception queue. An adjuster then spends 30 seconds reviewing the flagged issue, not 15 minutes keying in data from a perfect document.