Automate Tax Document Intake For Your Accounting Firm

Most accounting firms rely on client portals like CCH Axcess or secure file-sharing tools like ShareFile. These platforms are digital filing cabinets. They centralize documents but do not extract information from them, leaving skilled staff to perform hours of tedious data entry.

Here is a common scenario. An 8-person firm receives a single 150-page PDF from a high-net-worth client. A junior accountant spends half a day splitting the PDF, identifying W2s, 1099s, and brokerage statements, and manually keying numbers into tax software. They miskey a single digit from a 1099-B, triggering an IRS notice six months later and requiring non-billable hours to resolve.

The fundamental issue is that portal and file-sharing tools solve for storage, not processing. They shift the administrative burden of organization and data entry onto expensive human capital. This creates a severe bottleneck during tax season, limits firm capacity, and introduces a high risk of human error.

Syntora would approach tax document automation by first auditing the client's current document sources, whether a dedicated email inbox or a client portal with API access like ShareFile. For unstructured documents, the system would utilize AWS Textract for Optical Character Recognition (OCR), designed to reliably handle various resolutions. A custom classification model would then be developed to identify common tax form types, such as W2s, 1099s, or K-1s.

For each identified form, a tailored prompt would be sent to the Claude API to extract specific line items, like "Box 1 Wages" from a W2. Syntora would implement a validation layer using Python and Pydantic to check if extracted data matches expected formats, such as valid Employer Identification Numbers, and to perform checksums. This layer is designed to maintain data quality; we have built similar validation layers in other data extraction projects, ensuring high accuracy.

The verified, structured data would then be prepared for posting draft entries directly to QuickBooks Online via their API, or another specified accounting system. All original source documents and the extracted JSON data would be stored in a Supabase Postgres database, creating a permanent, searchable audit trail.

The entire workflow would be designed to run on AWS Lambda, triggered automatically by new file uploads. This serverless architecture means compute costs scale directly with usage. Syntora would implement structured logging with structlog and configure alerts for critical failures, such as an unknown document type being uploaded.

A typical engagement for this type of system would involve a discovery phase of 2-4 weeks, followed by a development and testing phase of 8-16 weeks, depending on document complexity and volume. Deliverables would include a deployed, custom-engineered system and comprehensive documentation. The client would need to provide access to example documents, existing document sources, and key personnel for requirements gathering and user acceptance testing.