Automate Client Intake for Your Accounting Firm

Most accounting firms use tools like Karbon or Practice Ignition for onboarding. Their web forms are great for structured data, but they break down when clients upload documents. A new business client might send a PDF of their articles of incorporation, a prior year's tax return, and a bank statement. These tools cannot read the PDFs, forcing a junior accountant to manually transcribe entity names, EINs, and bank details into the system.

Consider tax season onboarding. You send a 10-page PDF organizer to 150 new clients. Fifty of them print it, fill it out by hand, and scan it back. Your admin then spends weeks deciphering handwriting and typing W-2 box numbers into your tax software. If they mis-type a single digit on a Social Security Number, the e-filing will be rejected weeks later, causing a fire drill.

The structural problem is that these practice management tools are designed as systems of record, not systems of intelligence. Their data models are rigid. They can store a client's EIN, but they have no built-in capability to extract that EIN from a Form SS-4 PDF. They rely on perfect, manual human input. This architecture cannot handle the messy, unstructured reality of client-provided documents.

The result is expensive, low-value work that burns out your staff and delays the start of billable services. Every hour spent on manual data entry is an hour not spent on advisory. This process also introduces a significant risk of data errors that can lead to rejected tax filings or incorrect financial statements, damaging client trust.

The process starts with an audit of your current intake workflow. Syntora reviews every form you use and every document clients send. We map where each piece of data needs to go, from your practice management software to your billing system. This creates a clear data flow diagram that serves as the blueprint for the automation.

We would build a FastAPI service that uses an AI model, like one from the Claude API, to read and extract structured data from these documents. For your firm, this would be similar to the automated transaction categorization we built for our own accounting system, which processes thousands of bank transactions via Plaid integration. That system used a PostgreSQL ledger; your version would write client data to your existing practice management API. Pydantic models would enforce data validation, ensuring an EIN is always 9 digits before it ever touches your database.

The delivered system is a private API endpoint that your team can use. You would upload a client's PDF, and within 30 seconds, a structured JSON file is returned. This data can automatically create a new client profile, set up billing in Stripe, and generate an engagement letter. You receive the full Python source code, hosted on AWS Lambda for low-cost, serverless execution (typically under $50/month), and a runbook for maintenance.