Automate Invoice Matching and Bank Reconciliation

Most small businesses use QuickBooks Online or Xero for accounting. Their bank reconciliation tools are good at one thing: suggesting matches for transactions where the dollar amount is identical to an open invoice. This works for simple, one-to-one payments but breaks down in common scenarios. When a client pays three invoices with a single wire transfer, the total deposit amount does not match any single invoice, so the software flags it for manual review.

Consider a 15-person marketing agency that sends 80 invoices a month. A client makes a payment that is $50 short because their bank deducted a wire fee. The accounting software sees no exact match and cannot reconcile it. The agency's bookkeeper now has to manually find the invoice, identify the fee, and create a separate journal entry to account for the bank charge. This happens for 10-15% of their payments, consuming over 15 hours of work during every month-end close.

These platforms also lack the ability to read unstructured data. If an invoice PDF contains a specific project code, but the bank transaction description only has the client's name, there is no way for the software to connect them. The systems are designed for simple data points like date and amount. They cannot be taught the specific patterns of your business, like how a certain client always pays on the 15th or includes a specific reference number.

The structural problem is that off-the-shelf software is built for the average business, not your business. Its data model is fixed. You cannot add custom logic to handle payment processor fees, bundle payments, or parse project codes from invoice PDFs. This forces your finance person into a cycle of manual data entry and detective work that a purpose-built system can automate.

Syntora starts an engagement with a discovery process focused on your specific cash flow. We analyze 3-6 months of your bank transactions and a sample of 50 of your invoices. The goal is to map every payment pattern, fee structure, and data source. This audit produces a clear plan for the matching logic, identifying which data points are reliable signals for reconciliation.

We built our internal accounting system using Express.js and PostgreSQL, with Plaid handling bank synchronization. For your invoice matching system, we would extend this pattern. A Python service using the Claude API would watch for new invoices, use OCR to extract key data (invoice number, client, date, line items, amount), and store it in a structured format in a Supabase database. When Plaid reports a new bank transaction, the matching engine compares it against the open invoices, looking beyond just the dollar amount.

The delivered system is an automated reconciliation engine that feeds approved matches into your ledger. Instead of a manual checklist, your bookkeeper gets a dashboard showing transactions matched with over 99% confidence, ready for a single-click approval. The system flags the 5-10% of exceptions, like new clients or unusual payment structures, for human review. You receive the full source code and a runbook, operating on your own cloud infrastructure for a hosting cost under $50/month.