Automate Bookkeeping for Your Accounting Firm with Custom AI
A small accounting firm uses AI to automatically categorize transactions from bank feeds. AI also drafts monthly client reports and flags unusual spending patterns for review.
We recently built an automated reconciliation tool for a 5-person CPA firm. They were spending 20 hours a week manually matching transactions in QuickBooks. The system now processes client bank feeds daily, achieving 95% categorization accuracy and reducing manual review to just 2 hours a week.
This is not a generic SaaS tool but a custom system built for your firm's specific needs. Syntora builds Python-based solutions using the Claude API and Supabase to connect directly to data sources like Plaid or your accounting software. Our founder builds every system, ensuring production-grade quality from day one.
What Problem Does This Solve?
Most firms start with the built-in rules of QuickBooks Online or Xero. These systems are rigid, matching transactions based on simple text strings. A rule for 'STARBUCKS' won't catch 'SBUX #1234' or 'STARBUCKS COFFEE #5678'. Worse, these rules don't learn from your corrections and cannot be shared across clients, forcing you to rebuild them for each new account.
A firm with 30 small business clients faces this daily. A transaction for 'AMZN Mktp US' could be 'Software' for a marketing agency, 'Cost of Goods Sold' for a retail client, or 'Office Supplies' for a law firm. A rule-based system cannot handle this contextual difference. A bookkeeper must manually re-categorize dozens of these ambiguous transactions for every client, every month, turning reconciliation into a tedious, error-prone task.
Third-party tools like Dext or Hubdoc improve receipt capture but still rely on the same fragile rule-based logic for categorization. They add another monthly subscription fee without solving the core problem of contextual classification. You end up paying for a slightly better interface to the same manual workflow.
How Does It Work?
We start by connecting directly to your clients' data sources using APIs. This could be Plaid for bank feeds, Stripe for payment processing, or the QuickBooks Online API for existing accounting data. All raw transaction data (merchant name, date, amount) is pulled daily and stored in a secure, dedicated Supabase database that you control.
For each new transaction, a Python script constructs a prompt for the Claude API. This prompt includes the raw transaction text, the client's industry, their chart of accounts, and several examples of similar, correctly categorized transactions from that client's history. This 'few-shot' technique allows the AI to understand the unique context of each client's business without lengthy training.
The classification engine returns a suggested category and a confidence score from 0-100. Transactions scoring above 90% are posted automatically to your accounting software. Those between 70-90% are queued in a simple web app for one-click approval. Any transaction below 70% is flagged for manual review by your team. Every correction is stored and used as a future example, making the system smarter over time.
This entire workflow runs on a cloud server for about $40/month. We build a simple dashboard to track accuracy, processing volume, and time saved. The system writes approved transactions back to QuickBooks or Xero, so your team's core workflow never changes.
What Are the Key Benefits?
Categorize 95% of Transactions in 4 Weeks
Go from manual data entry to a live, automated bookkeeping system in one month. Stop waiting for end-of-month reconciliation and review transactions daily.
Margins Grow With Your Client Base
A scope-based build replaces monthly per-client SaaS fees. Your margins increase as you add clients, not your software bill.
You Own the Code and the Data
The complete Python codebase and Supabase database are yours. No vendor lock-in. Your client data never leaves your control.
Self-Improving with Every Correction
The system learns from your team's manual fixes. Accuracy increases over time without needing a developer to update categorization rules.
Works Directly with QuickBooks or Xero
We use official APIs to read and write data. Your team keeps working in their existing accounting software, with no new tools required.
What Does the Process Look Like?
Scoping & API Access (Week 1)
You provide read-only access to your primary accounting software and define the 2-3 most time-consuming bookkeeping workflows you want to automate.
Core Engine Build (Week 2)
We build the data pipeline from your sources to Supabase and develop the core transaction categorization model. You receive a link to a test environment.
Integration & Testing (Week 3)
We connect the system to write back to your accounting software and build the review dashboard. Your team tests with one client's live data.
Launch & Support (Week 4 Onward)
We roll the system out to all your clients. For the next 90 days, we provide hands-on support and fine-tune the algorithm based on real-world performance.
Frequently Asked Questions
- What determines the scope and timeline for a bookkeeping automation project?
- The scope depends on the number of data sources (bank feeds, credit cards, payment processors) and the complexity of your clients' charts of accounts. A typical project takes 4-6 weeks to build. Every engagement is scoped individually after a discovery call where we assess your current systems.
- What happens if the system categorizes something incorrectly?
- Transactions with low confidence scores are flagged for human review. If an incorrect categorization slips through, your team corrects it in your accounting software. Our system detects the change and uses it as a learning example for the future. During the 90-day support period, we fix any systemic issues.
- Is our client's financial data secure?
- Yes. We use Plaid for read-only bank connections, so we never see or store banking credentials. All data is encrypted in transit and at rest in your dedicated Supabase instance. You are the sole owner of the database. We sign an NDA for all engagements and follow strict data handling protocols.
- How does this compare to hiring a junior bookkeeper?
- A junior bookkeeper has fixed capacity and handles one task at a time. This system scales to handle hundreds of clients with minimal variable cost. It automates the most repetitive tasks, freeing your expert staff to focus on higher-value advisory services, client communication, and exception handling.
- Can this handle multi-currency transactions or complex invoices?
- The base system is designed for categorizing bank and credit card transactions. Automating multi-currency reconciliation or line-item extraction from PDF invoices are more complex tasks. We can build these, but they are scoped as separate projects. We recommend starting with transaction classification first.
- What do we need to provide to get started?
- The primary requirement is historical data. We need at least 12 months of previously categorized transactions from your accounting software to provide good examples for the AI. You also need to be on a QuickBooks or Xero plan that allows API access. We verify all prerequisites during our free discovery call at cal.com/syntora/discover.
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