Automate Accounts Payable Invoice Processing with Custom AI

Many accounting teams start with tools like Bill.com or Dext. These platforms offer basic invoice capture and bill payment but enforce a rigid, one-size-fits-all workflow. They are not designed for the operational complexity of a firm serving multiple clients, each with their own chart of accounts, approval chains, and reporting requirements.

Consider a 25-person firm managing AP for 15 clients. Client A requires any invoice over $5,000 to be approved by two partners. Client B requires invoices from specific vendors to be routed to a designated project manager for coding. Off-the-shelf software cannot manage this level of conditional logic. The team is forced to build manual workarounds outside the tool, exporting CSVs and managing approvals in email threads, which negates any efficiency gains.

The fundamental failure is that these products treat AP as a generic back-office task. For an accounting firm, AP processing is a client-facing service. A system that cannot adapt to client-specific rules is not a real solution; it just shifts the manual work to a different part of the process and creates reconciliation headaches.

Syntora would start by gaining a deep understanding of your current invoice processing workflows. This includes connecting to your invoices inbox, typically via IMAP using a Python library like `imap-tools`. We would pull a sample of 200-300 historical invoices and their corresponding entries from your accounting system, such as QuickBooks Online. This initial dataset would create the ground-truth for fine-tuning extraction logic and validating the accuracy of the proposed system.

For the core processing, Syntora would design a multi-step pipeline, often implemented with FastAPI and deployed on AWS Lambda. When a new invoice PDF arrives, it would trigger this pipeline. The Claude API would be used to extract key details like vendor name, invoice number, date, line items, and total amount, structuring this information as JSON. A subsequent Python function would validate the extracted data, checking for duplicates against a Supabase database table and verifying that line item totals match the grand total.

A custom rules engine, written in Python, would then apply your client-specific business logic. This engine would check the vendor name against a client mapping in Supabase to assign the correct GL code. For approvals, it would send a formatted Slack message with 'Approve' and 'Reject' buttons to the appropriate individual based on client and invoice amount thresholds. Upon approval, the system would push the bill directly into QuickBooks or Xero using their official APIs.

The delivered system would typically be a serverless application utilizing AWS Lambda for compute and Amazon S3 for secure invoice file storage. This architecture offers scalability and cost efficiency, with detailed cost projections provided during the architecture phase. Syntora would implement structured logging with `structlog` and CloudWatch alerts. If an invoice extraction fails after a predefined number of attempts, a notification would be sent to a designated Slack channel with the original PDF attached for manual review, ensuring no invoice is lost.