Integrate Custom AI into Your Tax Preparation Workflow

Many accounting firms use OCR tools built into platforms like CCH Axcess or Lacerte. These systems can extract data from a standard W-2, but they are not intelligent. They are template-matchers. When a client sends a single 50-page PDF containing a mix of brokerage statements, K-1s, closing documents, and scanned receipts, the software fails. The initial, time-consuming task of sorting, splitting, and identifying each document still falls to a human.

Consider the workflow for a client with multiple investment properties. An associate receives a single PDF with 12 different documents inside. They must manually split the PDF into individual files, name each one, then open the tax software and key in data from each document into the correct forms. A single complex K-1 can have dozens of fields. This entire process takes 45-60 minutes of low-value work per client before any actual tax strategy begins. The risk of data entry error from fatigue is high, especially during peak season.

The structural problem is that off-the-shelf tax software is built for mass-market compliance, not your firm's specific workflow or client base. Its data extraction models are generic and cannot be retrained on the unique document formats you see most often. You cannot teach it to recognize the K-1s from a specific real estate partnership your top 20 clients are invested in. Firms are forced to adapt their human processes to the software's limitations, creating expensive manual workarounds that don't scale.

The engagement starts with a document audit. Syntora would analyze a sample of 30-50 anonymized client files to identify the most common and time-consuming document types. We would map your existing data entry process step-by-step to understand exactly where the bottlenecks are. You receive a concrete proposal outlining 2-3 high-impact automation targets, like K-1 data extraction or 1099-B reconciliation, before any build begins.

The technical approach uses a FastAPI service running on AWS Lambda to create an AI document processing pipeline. When your team uploads a client's PDF, the service uses the Claude API to classify and split each document inside. It then extracts the relevant figures into a structured JSON format. This serverless architecture is highly efficient, often costing under $50 per month to process data for 500 client filings, and scales to zero when not in use.

The delivered system is a simple web portal your team uses to upload files. Within 60-90 seconds, it displays the extracted data, cleanly organized by tax form, with a side-by-side view of the source document for verification. This data can then be exported as a CSV or fed directly into your tax software's import function. The outcome transforms a 45-minute manual data entry task into a 5-minute review and approval step, freeing up your professional staff for higher-value advisory work.