Automate Tax Data Extraction with a Custom AI Agent

Accounting firms often rely on the receipt scanning in QuickBooks Online or Xero. These tools work for standard receipts but fail on multi-line invoices or vendor statements, frequently misclassifying sales tax or shipping costs. When a client sends a 20-page PDF bank statement, QBO's bank feed requires a CSV file, forcing a junior accountant to spend hours manually transcribing data, which is a primary source of reconciliation errors.

Consider an accounting firm managing books for 30 SMBs. At quarter-end, they receive a flood of documents: scanned receipts from a client's Dropbox, PDF bank statements, and emailed vendor invoices. An employee spends two full days per client manually keying data from bank statement PDFs into spreadsheets for import. A single mistyped digit on a withdrawal can trigger a 3-hour hunt for the reconciliation error. A complex supplier invoice must be manually split between Cost of Goods Sold and Office Supplies, hoping the allocation is correct.

The structural problem is that tools like Hubdoc or Dext use generic OCR models designed for high-volume, standardized documents. Their architecture is one-size-fits-all and cannot be fine-tuned for a specific client's unique invoice formats, like a construction company's AIA billing form. They lack the logic to apply client-specific chart of accounts rules during extraction, so the data they produce still requires significant manual cleanup and categorization before it can be used for tax prep.

The process begins with a document audit. You provide 5-10 anonymized examples of each document type you process, such as bank statements, vendor invoices, or 1099s. Syntora analyzes the layouts and identifies the specific fields required for your journal entries. This audit produces a set of data schemas and validation rules tailored to your chart of accounts.

Syntora would build the extraction agent using the Claude API, which excels at parsing structured data from PDF documents. The agent would be deployed on AWS Lambda for event-driven processing, ensuring you only pay for compute time when a document is being processed. A simple FastAPI endpoint provides a way to upload files or forward emails for automatic ingestion. Pydantic models enforce strict data validation, ensuring the output precisely matches the schema your PostgreSQL ledger expects.

The delivered system is a secure API that integrates with your existing workflow. When you forward an email with a PDF invoice, the system extracts the data within 15 seconds, validates it against your accounting rules, and creates a draft journal entry for review. You receive the full Python source code, a deployment runbook, and complete control over the system.