Automate Time Tracking & Billing with a Custom AI API

Professional services firms often start with a standard time tracker like Harvest or Clockify and an accounting tool like QuickBooks Online. The built-in integrations handle the basics: they can create a simple invoice from total project hours. This system breaks down as soon as billing logic becomes more complex than `hours * rate`. The standard connectors lack the ability to apply conditional logic or interpret unstructured text.

Consider a 15-person consulting firm that tracks time in Toggl. At the end of the month, an operations manager exports a CSV with 500+ time entries. They spend the next two days in a spreadsheet manually categorizing each line. 'Client strategy call' needs to map to the 'Consulting Services' item in QuickBooks, while 'Internal project kickoff' is non-billable. Pass-through expenses logged as notes must be separated and added as distinct line items. This manual process is the direct cause of invoicing delays and revenue leakage from miscategorized entries.

Third-party connectors attempt to solve this, but they are built on rigid, predefined data models. They can map a user in Toggl to an employee in QuickBooks, but they cannot read a task description like 'Q3 planning session w/ Client X' and correctly assign it to the 'Q3 Retainer' service item. These tools fail because they cannot bridge the semantic gap between how your team describes their work and how your accounting system needs to bill for it. The core problem is that your business logic is unique, and off-the-shelf software is, by definition, generic.

The engagement would begin with a discovery phase to map your firm's complete billing workflow. Syntora would audit your time tracking data and QuickBooks setup to document every rule, exception, and category. This audit produces a clear data mapping specification that you approve before any code is written. We've built document processing pipelines using the Claude API for financial analysis, and the same pattern applies here to interpret unstructured time entry descriptions.

The technical approach would be a serverless function on AWS Lambda that runs on a schedule or via a webhook. This function fetches data from your time tracking tool's API. For each entry, it uses the Claude API to parse the description and apply your specific business logic, categorizing it as billable, non-billable, retainer, or project-based work. The system then uses the QuickBooks API to create a detailed draft invoice, ready for a one-click review and approval. All processing rules and transaction logs would be stored in a Supabase database for auditability.

The delivered system is a piece of infrastructure that you own completely. It operates automatically in the background, requires no new software for your team to learn, and costs under $30 per month to run. Your finance team stops manipulating spreadsheets and starts the month with accurate, pre-populated draft invoices in QuickBooks. The billing cycle shortens from days to minutes.