Stop Manual Data Entry. Automate Accounting with Custom AI.

Most small businesses start by using the built-in rules engine in their accounting software, like QuickBooks Online. These rules work for simple categorizations, like flagging every transaction from 'Chevron' as 'Fuel'. But they cannot handle line-item splits. An invoice from a supplier with materials for three different client jobs still requires manual entry to allocate costs correctly.

This leads teams to dedicated document capture tools like Dext or Hubdoc. These are effective for basic receipt scanning but fail on complex documents. They cannot reliably parse multi-page invoices, match line items to purchase orders, or extract the custom fields required by an industry-specific ERP. This forces your bookkeeper to correct the data, defeating the purpose of the tool. Their per-document pricing also penalizes businesses with high transaction volumes.

For a construction firm processing 300 multi-line invoices a month, this is a real bottleneck. The bookkeeper spends 25 hours a month manually splitting transactions in QBO for job costing. The existing tools can't automate this critical workflow, creating delays for month-end closing and inaccurate project profitability reports.

Syntora approaches accounting automation projects by first understanding your specific document types, vendor variations, and existing accounting workflows. We would start with a discovery phase to audit your current manual processes and collect representative samples of your invoices and related financial documents. This initial step informs the architecture design and ensures alignment with your operational needs.

The technical approach would involve building a custom document processing pipeline. This pipeline would typically use Python and a robust OCR engine to digitize documents, preparing them for data extraction via the Claude 3 Sonnet API. Prompt engineering for the Claude API would be tailored to extract granular data points, including header information, line items, SKU numbers, and job codes, based on your business requirements. While precise accuracy is validated during development, the Claude API is known for high performance in structured data extraction.

The core of the system would be a FastAPI service. This service would orchestrate the entire workflow: when a new document is introduced, an AWS Lambda function would trigger the processing. The service would call the OCR and Claude API pipeline to obtain structured JSON data, which would then be validated against predefined business rules. For instance, extracted line items could be matched against open purchase orders stored in a Supabase Postgres database.

Integration with your existing accounting system is a critical component. The FastAPI service would connect to your accounting system's API (e.g., NetSuite, Acumatica, or a custom ERP) to create new bills, code line items correctly, and attach them to relevant customer jobs. The delivered system would be containerized and deployed within your own cloud environment, such as AWS, ensuring your financial data remains under your direct control.

For quality assurance, the system would include an exception handling queue. If the Claude API returns a confidence score below a specified threshold for any extracted field, the document would be flagged for human review. Structured logging, possibly using structlog, would provide an auditable trail for every document processed, supporting regulatory compliance and operational transparency. The goal is to reduce manual effort significantly while maintaining data integrity.