Automate Time Entry and Expense Categorization with Custom AI

Most professional service firms rely on tools like Harvest or QuickBooks Time. These are effective digital stopwatches but lack operational context. A two-hour block on a consultant's calendar for 'Client Meeting' doesn't tell the system which of the three active projects for that client it belongs to, or if part of that time was non-billable travel. The burden of categorization falls entirely on the employee, who must remember the context days later.

For expenses, a tool like Expensify brilliantly scans receipts for vendor and amount, but its project coding is manual. It cannot infer that a lunch receipt should be coded to 'Project Apollo' just because the consultant had a calendar event with that client an hour prior. A consultant at a 20-person firm spends 45 minutes every Friday cross-referencing their calendar, receipts, and project list to manually code everything correctly. They often forget to log 15-minute tasks like critical client emails, leading to thousands in lost billable revenue per person annually.

The structural problem is that these off-the-shelf tools are siloed. Harvest knows about time, Expensify knows about receipts, and your project management tool knows about tasks. None of them are designed to fuse these disparate data streams to understand the full context of a consultant's workday. The problem is not one of better data entry, but of automated context synthesis. This requires an AI-native approach, not just better integrations between rigid, rule-based applications.

The first step would be a data audit. We would connect to your core systems (Google Workspace or Microsoft 365, QuickBooks, project management tools) with read-only access to map the flow of information. The goal is to identify the specific text patterns and event correlations that signal billable work for your firm. We've used a similar document processing pattern to analyze financial reports, and the same technical approach applies here. You receive a data-flow diagram before any build work begins.

The technical architecture would use scheduled AWS Lambda functions written in Python to pull daily activity data from each source API. This unstructured text from calendars, emails, and tickets is then processed by the Claude API, which generates a structured JSON object containing suggested time entries and expense categorizations. We use Pydantic schemas to validate this output before it is formatted for the QuickBooks API, preventing data mismatches.

The delivered system provides each employee a daily email with a link to a simple web interface for a 2-minute review. With one click, they can approve the draft entries, which sync directly to QuickBooks. The entire system would run in your own cloud account for under $50 per month, and you receive the full source code and a runbook for maintenance. There is no ongoing license fee to Syntora.