Custom AI for Invoice Data Entry and Reconciliation

Many accounting firms rely on the built-in receipt capture in QuickBooks Online or entry-level tools like Dext. These are designed for simple, single-item receipts and fail on multi-line invoices from suppliers. When they encounter a complex table or a vendor-specific layout, the OCR misreads fields, forcing a full manual correction. Even dedicated AP platforms like Bill.com impose a rigid workflow that may not fit your firm's specific approval process.

Consider an accounting firm with a construction client. Each month, 300 invoices arrive from dozens of subcontractors, each in a different format. An accountant spends hours manually typing line items like '15 hours - framing' and '8 units - wiring harness' into QuickBooks. They then hunt through emails for a corresponding purchase order to match against, a process that takes 5-10 minutes per invoice. This amounts to over 30 hours of low-value clerical work every month.

The structural issue is that off-the-shelf tools use template-based OCR and fixed business logic. They are built for the 80% case. When a new invoice format appears, or if you need to match an invoice to a project using a custom ID field when the PO is missing, the system breaks. These products are software-as-a-service, not adaptable engineering systems; you cannot train them on your specific set of difficult vendors or inject your firm's unique validation rules.

The result is not just wasted time. Data entry mistakes lead to incorrect financial statements, making the month-end close painful. Delayed invoice processing can harm vendor relationships and cause you to miss early payment discounts. The manual bottleneck prevents your firm from taking on more clients because your most skilled people are stuck doing data entry.

The process would begin with an audit of your current invoice workflow. Syntora would review a sample of 50-100 invoices to analyze format variability and map your existing matching rules, from purchase orders to general ledger accounts. Syntora applied a similar discovery process when building its own internal accounting system, which used Express.js and PostgreSQL to map bank transaction descriptions from Plaid to specific ledger accounts.

The technical approach for your system would use a Python service with an OCR library for initial text extraction, paired with a large language model like Claude for intelligent parsing. This combination understands the context of an invoice, correctly identifying fields even when labels vary. The service would run on AWS Lambda for cost-effective processing, with a FastAPI endpoint to manage submissions. Processing time per page would typically be under 15 seconds, and Pydantic schemas would validate all extracted data for integrity.

The delivered system is a private API that integrates with your workflow. A new invoice emailed to a specific address could automatically trigger processing. The validated, structured data would be pushed to your accounting software as a draft bill, with the original PDF attached for reference. Any invoice with a validation confidence score below 95% or with mismatched totals gets flagged in a simple review queue for a human to approve.