Build a Custom AI Document Intake System

Many accounting firms rely on general-purpose client portals. These systems are secure file lockers, not processing engines. They provide a place for clients to drop unsorted documents, creating a bottleneck where staff must manually download, open, and key data from each file into accounting software. This is non-billable, error-prone work.

Some firms try generic OCR tools. These tools turn a PDF into a wall of unstructured text, losing all tabular data from a bank statement or multi-line invoice. The output is useless for direct entry into QuickBooks because the tool cannot distinguish an invoice number from a date or a line item from a subtotal.

Off-the-shelf document processing platforms like Dext or Hubdoc work for standard invoices but are rigid. They fail on non-standard documents, have processing delays of minutes or hours, and charge per-document fees that penalize high-volume firms. Their fixed rules cannot handle a client who sends a mix of 10 different document types in one batch, forcing staff back to manual processing.

Syntora would begin by thoroughly mapping your existing document workflow and defining precise data schemas for each document type you need processed. This discovery phase ensures the system is aligned with your specific operational requirements and accounting software.

Syntora would then design and build a secure client-facing portal, utilizing technologies such as Vercel and Supabase for robust authentication. This portal would provide each of your clients a dedicated and private upload point for their documents. The system would be engineered to handle file uploads efficiently, triggering backend processing upon document submission.

Uploaded documents would be processed by an AWS Lambda function, which would send them to AWS Textract for Optical Character Recognition (OCR). AWS Textract is designed to return structured JSON, including table data. This structured text would then be passed to the Claude 3 Sonnet API. Syntora would engineer and fine-tune specific prompts for the Claude API to accurately extract key-value pairs and line items relevant to your accounting categories.

A FastAPI service would receive and process the JSON output from Claude. Pydantic models would be implemented to validate the extracted data, verifying the correct format for fields like dates and monetary amounts. This service would then integrate with your chosen accounting software, such as the QuickBooks Online API, to create draft transactions and link the original source PDFs for easy verification and audit trails.

Monitoring and observability would be a core component of the delivered system. Syntora would implement tools like structlog for structured logging and Sentry for error tracking, configuring alerts for any critical system events or API failures. All custom Python source code for the system would be delivered to you in your private GitHub repository, ensuring full ownership and transparency.