Automate Your Manual Data Entry with a Custom AI System

Most teams start with manual data entry. An admin spends hours a day copying information from PDFs into a CRM or spreadsheet. This is slow, expensive, and the error rate from typos can be as high as 5%, causing costly downstream problems. When volume increases, the only solution is to hire more people for the same repetitive task.

A regional insurance agency with 6 adjusters faced this exact issue. An administrator spent four hours daily processing 50 emailed claim forms. They had to open each PDF, find 10 specific fields, and type them into a claims management system. Any typo in a policy number or date of loss created hours of rework for an adjuster, delaying the entire claims process.

Off-the-shelf OCR tools seem like a solution, but they fail on interpretation. They can extract raw text from a PDF but cannot reliably identify which number is the 'Invoice Total' versus the 'Subtotal' across different layouts. These tools lack the contextual understanding to handle varied formats, forcing you back to manual review and correction, defeating the purpose of automation.

Syntora would begin an engagement by collecting a representative set of 50-100 of your documents, covering all major formats and layouts. We would use Python with the pdfplumber library for clean text extraction. This corpus of documents would serve as the ground truth for building and testing the AI model, ensuring it handles the specific variations your business encounters.

The system's core would be a Python service built with FastAPI that sends extracted text to the Claude API. We would craft a precise prompt that instructs the AI to find specific fields and return them as structured JSON, handling variations in wording like 'Invoice No.' versus 'Reference #'. For low-quality scans, the system would first process the image with AWS Textract for superior OCR before passing the text to Claude. This two-stage approach is designed to achieve high accuracy on difficult documents.

This FastAPI service would be deployed on AWS Lambda, which keeps hosting costs low for most workloads. We would then build the integration pipeline. A trigger would monitor a specific email inbox or cloud storage folder. When a new document arrived, the Lambda function would be invoked, and the extracted data would be posted directly to your target system, such as a Salesforce CRM or a custom ERP, using the httpx library for reliable, asynchronous API calls.

For quality control, every successful extraction would be logged to a Supabase database for auditing. If the Claude API returned a confidence score below 0.9 for any field, the document would be automatically flagged and sent to a simple review queue for human verification. We would use structlog for detailed, structured logs, so every document's journey through the system would be traceable.