Build a Custom AI Pipeline for Your Accounts Payable

Many teams start by manually keying PDF invoices into QuickBooks or Xero. This is manageable at 50 invoices a month, but at 300 it becomes a full-time job with a 3-5% error rate from typos. Off-the-shelf AP automation tools promise a fix, but they charge per invoice. At $0.50 per document, 500 invoices a month add a recurring $250 software bill that grows with your business.

Consider a regional distributor with 30 employees using NetSuite. They receive around 400 invoices per month. They tried an AP SaaS product, but it failed on 20% of their invoices from smaller vendors who use inconsistent templates. The system couldn't reliably find the invoice number or line-item totals, forcing the accounting clerk to manually review and correct nearly 80 documents each month, defeating the purpose.

The core problem is that generic tools are trained on generic data. They cannot learn the specific layouts of your top 10 vendors or understand your internal logic for assigning GL codes. They offer light customization through rules, but you cannot rewrite the core data extraction model. You are stuck with its performance, paying for every document it processes, even the ones it gets wrong.

Syntora would start an engagement by auditing your existing accounts payable workflow and reviewing a representative sample of your vendor invoices. This allows us to understand the range of formats, identify critical data fields, and map out integration points with your accounting software.

The approach would centralize invoice intake. Syntora would configure a Python script using AWS SES and Lambda to automatically pull new invoices from a dedicated email address, such as 'invoices@yourcompany.com'. This design streamlines initial receipt, eliminating manual forwarding.

The core of the system would be a data extraction pipeline. For each PDF, an OCR process converts the image to text. We have built document processing pipelines using Claude API for financial documents, and the same pattern applies to extracting data from accounts payable invoices. The Claude API would be prompted to identify and extract key fields like vendor name, invoice number, date, due date, line items, and totals. This process is engineered for rapid execution, typically within seconds, producing structured JSON output.

Syntora would wrap this extraction logic in a FastAPI application. The extracted data would then be used to create a draft bill in your accounting system via its API, for example, the QuickBooks Online API. Supabase would serve as a lightweight database to log every transaction, store the extracted data, and link back to the original PDF, providing a full audit trail. The application would be deployed on AWS Lambda, which is designed for cost-effective operation for up to thousands of invoices monthly.

A simple web interface would be built for your accounting team. This interface would allow review of any invoices where the AI identifies low-confidence extractions (e.g., below a 95% certainty threshold on a key field). The interface would display the original PDF alongside the extracted fields, enabling quick corrections. This human-in-the-loop mechanism is critical for maintaining high accuracy and continuously improving the system's performance.