Custom AI for Accounting Invoice Processing

Many accounting offices rely on QuickBooks Online or Bill.com for invoice management. QBO's automation is based on rigid rules that fail when a vendor name is slightly different. Bill.com offers 'AI' that is often just template-based OCR, which struggles with non-standard invoice layouts and cannot extract complex line-item details accurately. Both platforms lock you into simple, linear approval chains that cannot handle real-world business logic.

Consider a 20-person office processing 500 invoices per month. An AP clerk spends the first week of every month manually keying data from PDFs into the accounting system. This involves 10-15 minutes per invoice of pure data entry, followed by emailing a manager for approval. That manager receives an email with a PDF attachment and no context, forcing them to find the original purchase order in a separate spreadsheet to verify the line items. The risk of a data entry error is high, and the process is slow.

The structural problem is that these off-the-shelf tools are built with a fixed data model for the most common use case. They cannot execute custom validation logic, such as checking an invoice against a specific project budget stored in another system. If you need to enforce a rule like, 'For all AWS invoices, verify the listed account ID is one of our 5 active IDs before routing for approval,' these tools provide no solution. You are forced to perform these critical checks manually outside the system.

The engagement starts with a discovery and audit phase. Syntora analyzes a sample of 50-100 of your most common invoices to map out every vendor format, required data field, and existing approval path. This process produces a detailed scoping document that defines the complete extraction and validation logic before any code is written, ensuring the final system meets your exact needs.

The technical approach would use a FastAPI service written in Python to orchestrate the workflow. For data extraction, the Claude API reads and structures data from any PDF invoice, eliminating the need for brittle, template-based OCR. The extracted data is then validated against your business rules and existing ledger, which can be managed in a Supabase PostgreSQL database. AWS Lambda functions handle the event-driven processing tasks, providing a cost-effective way to run the system.

The delivered system is a dedicated service that integrates into your existing workflow. Your AP team emails or uploads invoices, and the system processes them in under 60 seconds. Any exceptions or low-confidence extractions are flagged in a simple dashboard for human review. You receive the full source code and a maintenance runbook, and the system runs on your own cloud infrastructure, ensuring you have full control.