Automate Invoice Data Entry and Matching with a Custom AI System

Small accounting firms often rely on tools like QuickBooks Online's receipt capture or add-ons like Dext and Bill.com. These tools work for standard, machine-readable invoices and receipts. They fail when faced with non-standard formats, such as PDFs from local suppliers that are scanned images, or invoices that consolidate multiple purchase orders into one bill. Their optical character recognition (OCR) is generic and often misinterprets fields, requiring manual correction.

Consider a 10-person firm managing books for construction clients. A client submits a single invoice from a materials supplier that covers three different job sites, referencing three separate PO numbers in the notes field. Bill.com will extract the total amount but cannot split the line items and allocate them correctly to the three jobs. An accountant has to open the PDF, find the POs, log into the accounting system, and manually create three separate bills. This 2-minute task, repeated 30 times a day, consumes 10 hours of a bookkeeper's week.

The core problem is that off-the-shelf tools are built for the 80% case: clean, templated invoices. Their data models are fixed. You cannot add a rule that says 'If the vendor is Supplier X and the notes field contains a PO number starting with JOB-, then split the line items by job.' These platforms are designed for mass-market scale, which prevents them from handling the specific, high-variance exceptions that define a firm's real workflow.

As a result, firms are stuck with 'human middleware.' Accountants spend their time correcting OCR errors and manually matching invoices instead of providing higher-value advisory services. This caps the number of clients a firm can handle per employee and makes scaling dependent on hiring more data entry staff, not on improving process efficiency.

The process would begin with an audit of your current invoice workflow. Syntora would analyze a sample of 100-200 recent invoices to identify the most common formats and the most frequent sources of exceptions. We would map out exactly how you match invoices to purchase orders and payments today. This discovery phase produces a clear technical plan for what data to extract and what validation rules the AI needs to apply.

The technical solution involves a FastAPI service that listens to a dedicated email inbox or cloud storage folder. When a new invoice PDF arrives, an AWS Lambda function triggers, passing the document to the Claude API for data extraction. Claude is specified because its large context window can handle multi-page invoices and its instruction-following capability is excellent for extracting structured data from unstructured text. The extracted data is then validated against your existing client and PO data in a Supabase (PostgreSQL) database.

The final system would write validated journal entries directly into a double-entry ledger, which we can build or connect to your existing one. You would get a simple dashboard, built with Vercel, to review exceptions. Invoices processed successfully (over 95% of volume) require no human touch. The system costs under $100 per month to host on AWS and Supabase. You receive all the Python source code and a runbook detailing how to manage the system.