Automate Client Document Collection and Verification

Most accounting firms use document management systems like CCH Axcess or Thomson Reuters GoFileRoom combined with their tax prep software. These systems are good for storage and retrieval but do not intelligently process the contents of a document. Their built-in OCR can pull text from a simple invoice but fails to understand the structure of a multi-page K-1 schedule or a consolidated brokerage statement. The software cannot distinguish 'Box 1: Wages' from 'Box 12a: Uncollected social security tax'.

For example, during tax season a new client emails a single 25-page PDF containing their W-2, three 1099-DIVs, a 1099-INT, and a brokerage statement. A junior accountant must download the file, manually identify each form, and re-type 50+ different values into tax software like Drake or Lacerte. This 45-minute process is repeated for every new client, and a single typo in a cost basis or an EIN can trigger an IRS notice six months later, damaging the client relationship.

The structural problem is that document storage and tax prep tools are designed for archival and calculation, not for data extraction. Their architecture is based on file metadata, not the semantic content within the files. They lack the specialized AI models required to parse the complex, variable layouts of government and financial forms. Adding this capability would require a complete product overhaul, so they continue to rely on error-prone manual entry.

The first step is a document audit. Syntora would analyze 5-10 anonymized examples of your most common client documents, from simple W-2s to complex multi-page brokerage statements. We map every required field to its corresponding box in your tax software. This audit defines the exact data extraction requirements and the integration points for your existing workflow.

An AWS Lambda function would trigger a FastAPI service to process each uploaded document using the Claude API. The AI identifies the form type and extracts data as key-value pairs, which are then validated against Pydantic schemas to ensure data integrity. This approach can reduce data entry errors by over 95% before a human ever sees the output. Processing a 20-page document package typically completes in under 60 seconds.

The delivered system provides a simple verification screen where an accountant reviews the extracted data next to the original PDF. A single click approves the data and pushes it into the client's file in your practice management software, completely removing manual typing. You receive the full Python source code and documentation, running on a system that costs under $150 per month to operate for up to 5,000 documents.