Automate Accounts Payable and Receivable Reconciliation with a Custom AI System

Most small businesses rely on the bank rules in QuickBooks or Xero. These tools work for simple, one-to-one matches but fail with common complexities. A rule that looks for an exact dollar amount or vendor name cannot reconcile a single ACH deposit that bundles payments for three different invoices. The system sees one lump sum and cannot connect it to multiple open items, forcing manual intervention.

Consider a 15-person B2B services firm that sends 200 invoices a month. Payments arrive via Stripe, ACH, and wire transfer. When a client pays three invoices with a single wire of $15,350, the QuickBooks bank feed shows one transaction. A bookkeeper must then manually search emails or remittance advice to break down that payment, apply it to the correct invoices, and account for any credit memos. This 15-minute task, repeated dozens of times, consumes over 8 hours of valuable time each month.

Tools like Bill.com or Melio help manage bill payments but do not solve the core reconciliation problem for accounts receivable. They can send payment data to an accounting system, but they cannot reconcile payments that originate outside their platform. The structural issue is that these off-the-shelf products are built with rigid, rule-based logic. They lack the ability to handle the many-to-one or many-to-many relationships inherent in real-world B2B payments, forcing your team to fill the gaps manually.

Syntora's approach starts by mapping your entire cash flow. We analyze how you issue invoices, where payments land (Stripe, Plaid-connected bank accounts), and how invoices are formatted (PDFs from email, structured data from an API). This audit identifies the specific matching logic needed, such as handling bundled ACH deposits or fees deducted by payment processors.

A custom reconciliation system uses a combination of technologies chosen for the task. An OCR tool like AWS Textract can extract line-item data from PDF invoices with over 99% accuracy. This structured data feeds into a FastAPI service that uses a Python matching engine to compare invoice details against transaction data. For matching vendor names with slight variations, sentence-transformer models can find semantic similarities. The entire workflow would run on AWS Lambda, processing each new transaction in under 500 milliseconds.

The delivered system operates automatically in the background. Syntora has direct experience building the core of such a system. We built our own accounting platform using Express.js and PostgreSQL, which features a 12-tab dashboard for managing journal entries and monthly close workflows. A custom reconciliation engine would build on this foundation, writing cleared transactions to your existing accounting system's API as categorized journal entries. This provides a complete audit trail for over 5,000 transactions per month.