Automate Invoice Processing with a Custom AI System

Many small finance teams rely on the receipt capture feature in QuickBooks Online. The tool extracts header-level data but frequently misclassifies line items or fails entirely on multi-page invoices. An accountant ends up correcting more than half the entries, defeating the purpose of the automation.

Platforms like Bill.com or Melio offer more advanced workflows, but their data extraction is a black box. When an invoice for AWS is categorized as "Office Supplies," you can only fix that single entry. You cannot teach the model the correct general ledger code for that vendor, forcing you to make the same manual correction every month. Their approval routing is also rigid, requiring your team to adapt its process to the software's limitations.

Consider a 2-person finance team at a 30-person agency processing 200 vendor invoices a month. A freelance designer emails a 3-page PDF where line items on pages 2 and 3 must be billed to different client projects. QBO's OCR only reads the first-page total, so the accountant must open the PDF, manually create split journal entries, and re-attach the file. This 10-minute task consumes over 30 hours of valuable time each month.

The structural issue is that off-the-shelf tools are built for the most common denominator. They use generic models trained on millions of invoices, not your specific vendor formats. You cannot add custom validation logic, like requiring a PO number for any invoice over $1,000. You are renting a generic process instead of owning an intelligent system tailored to your business rules.

The first step is a discovery audit of your current invoice workflow. Syntora reviews 10-15 examples of your most common and problematic vendor invoices to map out required fields, validation rules, and GL coding logic. This process produces a clear data schema and a fixed-scope build plan for a 4-week build cycle before any code is written.

The technical approach uses Python and the Claude API for intelligent document extraction. A FastAPI endpoint receives emailed invoices or file uploads from a simple web interface. The system sends the document to Claude with a precise prompt engineered to extract line items, PO numbers, and vendor details into a structured JSON format. Pydantic models then validate the extracted data against your required schema, catching errors before they ever reach your ledger. The entire pipeline runs on AWS Lambda, processing a typical invoice in under 15 seconds.

The delivered system is a simple web dashboard where your team reviews extracted invoice data. Correct entries are approved with one click, which creates the bill and journal entries in your accounting software via its API. You receive the full source code, a runbook for maintenance, and an architecture that lives in your own AWS account. Hosting costs for processing up to 1,000 invoices per month are under $50.