Automate Your Accounts Payable with Custom AI Engineering

Many small finance teams rely on the built-in rules of their accounting software, like QuickBooks Online. These tools can automate recurring payments from major vendors but fail with variable invoices. QBO's rules are based on simple text matching, so an invoice from a new contractor with multiple line items that need to be split-coded requires manual entry and review. The system cannot intelligently parse a document it has not seen before.

Dedicated AP tools like Bill.com or Melio offer more features, but their optical character recognition (OCR) often fails on non-standard PDF formats. A multi-page invoice from a small supplier can result in merged line items, incorrect due dates, or misread amounts. This forces your team to manually correct the extracted data, defeating the purpose of the automation. These sync failures can also create duplicate entries in your ledger, leading to hours of painful reconciliation work at the end of the month.

Consider a 20-person agency with one person managing finance. They receive 150 invoices a month. A key vendor submits a PDF with three line items that must be coded to different expense accounts. The AP tool merges them into a single line item. The finance manager must then log in, manually split the entry, look up the correct GL codes, and push the change to the accounting system. This happens 20-30 times a month, consuming a full day of high-value time.

The structural problem is that off-the-shelf tools use template-based OCR designed for the most common invoice layouts. They are not built with modern language models that can understand the content and structure of a document contextually. To truly automate AP, you need a system that can read an invoice like a human does, not just match text to a predefined template.

The first step is an audit of your existing AP process. Syntora would analyze a batch of 100 recent invoices to map out the different formats, vendors, and required general ledger codes. We identify the specific data points that need to be extracted and the business logic for your approval workflow. This initial analysis defines the exact scope before any code is written.

A custom system would use a modern language model via the Claude API for data extraction, wrapped in a FastAPI service deployed on AWS Lambda. This approach is superior to traditional OCR because it understands context, correctly identifying line items and dates from invoices it has never seen. Pydantic schemas validate all extracted data, ensuring amounts are correct and dates are in the right format before anything is written to your ledger. This architecture can process a typical invoice in under 3 seconds.

The delivered system monitors a dedicated inbox like invoices@yourcompany.com. When an email with an invoice arrives, it is automatically processed. A simple dashboard shows all processed invoices, with any exceptions (e.g., confidence score below 95%) flagged for a 1-click review. Once approved, the data is pushed directly into your accounting software. The system runs on serverless infrastructure, keeping monthly hosting costs under $50, and a typical build takes 2-3 weeks.