Build Custom Automation That Fits Your Exact Business Process

Many businesses start with a SaaS document parsing tool. These tools are great for standard invoices or receipts, but they fail when faced with industry-specific business rules. For example, an insurance claim form might require that if the 'Total Loss' box is checked, at least three photos with specific naming conventions must be attached. A generic parser cannot enforce this conditional logic.

Consider a regional insurance agency with 6 adjusters processing 200 claims per week. They tried a popular OCR tool to extract data from claim forms. The tool could pull text, but it could not cross-reference the extracted policy number with their internal Supabase database to verify coverage limits. This failure forced an adjuster to manually look up every single policy, defeating the purpose of automation.

The fundamental issue is that off-the-shelf tools are built for horizontal use cases. They provide a rigid, one-size-fits-all model for data extraction. They cannot execute custom validation code that queries your private database or calls a third-party API as part of the workflow. This limitation means your most important business rules are left to manual review.

Syntora's approach to automating document processing starts with a detailed mapping of every manual step and business rule into a technical specification. For document types like insurance claims, the system would typically use a library such as Python's pdf2image to convert incoming claim PDFs into a processable image format. Custom validation functions would then be defined, which could include using a client library like supabase-py to check policy status against a client's internal database.

The core of such a system would be a Python service built with FastAPI. For each document, the system would send the image to an AI API, such as Claude 3 Sonnet, with a structured prompt designed to extract specific fields. This API call, which can be made with the httpx library for asynchronous performance, typically returns structured JSON within a few seconds. The FastAPI service would then run the extracted data through the predefined validation functions.

This service would be packaged in a Docker container and deployed on a serverless platform like AWS Lambda, which can be triggered automatically when a new file is uploaded to an S3 bucket. This serverless architecture is well-suited for unpredictable workloads, scaling efficiently from zero to many concurrent documents. After processing and validation, the data would be written directly to the client's existing claims management system via its REST API.

For monitoring and operational insight, structlog would be used to output structured JSON logs to a service like AWS CloudWatch. CloudWatch Alarms could be configured to send notifications, for instance, if the extraction failure rate exceeds a defined threshold or if processing time consistently surpasses expected limits. This type of automated pipeline can significantly reduce manual effort and improve data accuracy.