Mitigate Accounts Payable Compliance Risks with Custom AI Automation

Small businesses often start with QuickBooks Online or Xero for bookkeeping. These tools depend on manual data entry for invoices received as email attachments. A bookkeeper might mis-key a vendor's tax ID or an invoice number. QuickBooks’ duplicate invoice check only flags exact matches on invoice number and vendor name; a simple typo like INV-101 versus lNV-101 bypasses the check entirely.

Consider a 10-person consulting firm receiving 100 invoices a month in a shared inbox. An employee manually types the vendor name, date, and amount from each PDF into Xero. One vendor, "ACME Services Inc.", is sometimes entered as "Acme Services" or "ACME Inc". At year-end, pulling a payment history for 1099 filing becomes a reconciliation nightmare. Payments to "Acme Services" ($450) and "ACME Inc." ($300) both fall under the $600 reporting threshold, but the actual total of $750 creates a clear tax compliance risk.

The structural issue is that off-the-shelf accounting systems are databases of record, not workflow engines. They don't control the data entry process itself. Services like Bill.com add a workflow layer, but their OCR is generic. It can extract an invoice total but often fails to parse line items or correctly identify a purchase order number. These systems cannot enforce custom business rules, such as requiring a PO number match for any invoice over $5,000 before scheduling payment.

The result is a constant, low-grade compliance risk. Duplicate payments slip through, vendor records are inconsistent, and the audit trail is fragmented across emails, PDFs, and the accounting system. This inefficiency exposes the business to financial loss from errors and potential IRS penalties for inaccurate 1099 filings.

The engagement starts with mapping your end-to-end accounts payable process. Syntora reviews how you receive invoices, what your approval workflow looks like, and how payments are issued. We analyze a sample of 50-100 past invoices to understand the variations in format and data. This audit produces a clear plan for an automated system that fits your exact operational needs.

The core of the system would be a FastAPI service using Python and the Claude API to read and structure data from incoming invoice PDFs. This approach uses a large language model to understand context, correctly identifying vendor names with slight variations and cross-referencing them against a canonical list in a Supabase (PostgreSQL) database. Pydantic models would enforce data validation, ensuring invoice numbers and dates conform to required formats before anything is written to the ledger.

For its own internal finance system, Syntora built the ledger on PostgreSQL and the API layer with Express.js to connect bank data via Plaid. For your accounts payable system, the delivered solution would be an AWS Lambda function triggered by new emails to your AP inbox. The system processes the invoice, runs validation checks, and creates a draft bill in your existing accounting software via its API. You receive the full source code and a runbook detailing how the system operates.