Stop Arguing About Faxes. Automate the Document Workflow.

Services like eFax or RingCentral Fax deliver a PDF to your inbox. This feels modern, but it just shifts the problem. Instead of a paper jam, you have an inbox full of attachments that someone has to open, read, and manually re-type into a CRM, EMR, or ERP system. The fax service solves the transmission but ignores the data entry work it creates.

A medical practice receiving 30 patient referrals a day via their HIPAA-compliant online fax service faces this daily. An admin assistant opens each PDF, identifies the patient name, referring doctor, and reason for visit, then copies it into their EMR. A single typo in a patient ID can delay care. At 5 minutes per fax, that is 2.5 hours of daily work that is both tedious and high-risk.

Teams then try off-the-shelf OCR software, but these tools are trained on high-resolution scans, not grainy, low-DPI faxes with smudges and alignment issues. They produce character error rates of 15-20%, forcing a human to proofread every single field. This defeats the purpose of automation and often makes the process slower than pure manual entry.

Syntora approaches automating data extraction from faxed documents with a focused engineering engagement. We begin with a discovery phase, auditing your specific document types, required data fields, and target business systems.

The core technical architecture would connect directly to your online fax service's email notifications. An AWS Lambda function would trigger upon new PDF arrivals, saving them to a secure S3 bucket. The system would use Python's pypdf library to split multi-page documents and Pillow to enhance image quality, improving accuracy before extraction.

Each processed page image would then be sent to the Claude 3 Sonnet API. Unlike traditional OCR, Claude understands document layout and context. We would define a precise JSON schema in the prompt to instruct Claude on extracting specific fields, such as 'Invoice Number', 'Patient Name', or 'Total Amount'. Syntora has experience building similar document processing pipelines using Claude API for various document types, including financial documents, and applies this pattern to new industries.

The extracted JSON data would undergo validation using Pydantic to ensure data integrity. A lightweight FastAPI service would then make a secure API call to your internal system—be it a CRM, ERP, or custom database—to push the validated data.

For operational visibility, all processing would be logged using structlog. Should Claude's confidence for any field fall below a predefined threshold or Pydantic validation fail, the system would flag the document for human review via a simple web interface. Original PDFs and extracted data would be stored in a Supabase table for a defined period, typically 30 days, for audit purposes before purging.

A typical engagement for a system of this complexity, processing 2-3 document types and integrating with one destination, usually involves a build timeline of 8-12 weeks. Client responsibilities would include providing access to their online fax service, sample documents, and API documentation for their destination system. Deliverables would encompass the deployed data extraction system, complete source code, and comprehensive documentation.