Calculate Your Consultancy's Automation ROI

Growing SMBs first hit the limits of manual processing. A team member spends half their day copying data from PDF invoices into an accounting system, introducing a 5-7% error rate from typos and fatigue. The process is slow, non-scalable, and depends entirely on one person being available.

A regional insurance agency with 6 adjusters tried to solve this with a shared inbox and a Google Sheet. Every claim form arrived as a PDF. An admin would open the PDF, manually type the policy number and incident date into a new row, and then upload the file to Google Drive. When the admin was on vacation for a week, 200 claims piled up, delaying customer responses by 5 business days.

The next step is often a simple script. Someone technical on the team writes a Python script using an open-source OCR library. It works on their local machine but breaks when a new document format appears. It has no error handling, no logging, and no one to maintain it when that person leaves. It is a temporary fix, not a production system.

Syntora would approach document processing automation by first mapping the end-to-end workflow, from how a document arrives to where the final extracted data needs to go. We would then analyze 50-100 sample documents to identify all field variations and define the precise data extraction requirements.

The core of the system would involve a Python service built with FastAPI to orchestrate the process. This service would receive a document, process it using the Claude API for its advanced document understanding capabilities, and then structure the extracted information into JSON data. We have experience building similar document processing pipelines using the Claude API for financial documents, and the same architectural patterns apply to other document types. The system would use httpx for asynchronous API calls, allowing it to efficiently handle typical bursts of document volume.

The FastAPI application would be deployed as a container on AWS Lambda, utilizing a serverless architecture that scales based on usage and typically keeps hosting costs under $50 per month for common volumes. The extracted data would be written directly to your chosen destination system, such as a Supabase database or a custom field in your CRM, via its API. All activity would be recorded using structlog for easy debugging and auditing.

The delivered system would include health checks and the ability to send alerts, such as a Slack notification, if processing failures occur. Syntora would develop a simple dashboard to show daily processing volume, average processing time, and the accuracy rate based on a sample of human-verified outputs, ensuring visibility into the system's ongoing performance.