Automate Time Tracking and Billing for Your Professional Services Firm

Many professional services firms use QuickBooks Time or Harvest. These tools are excellent for logging hours, but their project codes are rigid. They can't interpret the narrative description of a time entry to categorize it against a specific deliverable in a Statement of Work (SOW), forcing manual reconciliation.

Consider a 15-person consulting firm using Harvest for time tracking and QuickBooks for billing. Each month, an operations manager spends two full days exporting CSVs. They must read every single time entry to manually categorize the work against SOW line items for dozens of clients. For a client with a retainer, they manually calculate which hours are covered and which are overage, creating separate line items in the QuickBooks invoice. This 12-hour process is slow and error-prone; mis-categorizing just a few hours can result in under-billing by hundreds of dollars.

The structural problem is that off-the-shelf tools treat time tracking and billing as separate, linear steps. They lack a logic layer that understands the contractual context of the work performed. Their data models see hours and a project tag, but they cannot connect the description "Final review of Q3 strategy deck" to the SOW line item "Phase 2: Strategic Planning Deliverable." A human must act as the expensive, error-prone bridge between the work log and the invoice.

The engagement would begin with an audit of your standard SOWs and client billing rules. Syntora would map every variation: retainers, tiered hourly rates, pass-through costs, and overage policies. We'd then analyze three months of your team's time entries to understand the language and keywords they use to describe their work, forming the basis for the classification logic.

The technical core of the system would be a Python service using the Claude API. Claude's large context window allows it to read an entire SOW and then accurately classify free-text time entries against the correct line items. The classified data would be stored in a Supabase database. A FastAPI service then connects to the QuickBooks API to generate complete draft invoices, ready for a final 15-minute human review.

The delivered system would run on a schedule using AWS Lambda, typically costing under $30 per month to operate. You receive the full source code in your GitHub repository, a runbook for maintenance, and a simple dashboard to monitor the process. Your team continues using their existing time tracking tool without any changes to their workflow; the automation operates entirely in the background.