Automate Employee Expense Tracking with a Custom AI System

Many small companies start with tools like Expensify. The receipt scanning is helpful, but its automated categorization is generic. A software subscription might be classified as 'General Software,' but your chart of accounts needs to distinguish 'SaaS - Sales' from 'SaaS - Engineering'. This forces an accountant to manually re-categorize dozens of transactions at the end of each month.

Then there are modern corporate card platforms like Ramp. Ramp's rules engine is powerful but locked to its own card ecosystem. If you have expenses from employee-reimbursed ACH payments or invoices paid by wire, they live outside the system. This creates a separate, manual workflow for reimbursements, defeating the purpose of a unified platform and splitting your financial picture in two.

Consider a 20-person agency where an account manager uses a corporate card for a client lunch, a LinkedIn Ads campaign, and a software subscription. A standard tool categorizes all three as 'Business Expenses'. An accountant must then log in, find the receipts, and manually re-assign them to 'Meals & Entertainment', 'Client Advertising', and 'Marketing Software'. This 5-minute task, repeated 150 times a month across the team, consumes over 12 hours of accounting time.

The structural problem is that these platforms are built for horizontal scale, not vertical depth. Their data models are fixed to serve millions of customers with a generic chart of accounts. They cannot adapt to your company's specific financial reporting needs without creating manual workarounds. Their architecture assumes one-size-fits-all categorization, which is fundamentally incompatible with the detailed financial tracking a growing business requires.

The first step is an audit of your current expense workflow. Syntora reviews your chart of accounts, reimbursement policies, and existing software. This discovery phase maps every source of company spending, from corporate cards to wire transfers, to define the exact categorization logic needed. You receive a clear scope document outlining the data connections and the custom rules before any work begins.

We built a system for our own finances using Plaid for bank syncs and a PostgreSQL ledger. For your team, the approach would be similar: a FastAPI service listens for webhooks from Plaid when new transactions post. A call to the Claude API, prompted with your specific chart of accounts and categorization examples, assigns the correct expense category. The result is written as a journal entry into a Supabase PostgreSQL database, ready for export.

The delivered system is a private API that connects your data sources to your ledger. You get a simple dashboard to review categorized transactions and flag any exceptions for manual review, which is typically fewer than 5% of all transactions. The entire system runs on AWS Lambda, often costing under $20 per month to operate. You receive the full source code, documentation, and a runbook for maintenance.