Integrate AI with Your Time Tracking and Billing System

Professional services firms often use tools like Harvest or QuickBooks Time for logging hours. These tools are effective digital timesheets, but they treat the description field as simple text. They cannot interpret the meaning behind an entry like "Q3 strategy call with client" versus "Internal project kickoff meeting." This leaves the critical task of categorization to the employee, which is inconsistent, or a project manager, which is a manual bottleneck.

Consider a 15-person consulting firm that uses Clockify for time tracking and QuickBooks for invoicing. At the end of the month, a senior team member exports a CSV file with over 1,000 line items. They must then spend 20-30 hours manually reading each description, assigning it to the correct project code, verifying it against the client's PDF Statement of Work, and grouping it on the invoice. An entry for "Researching new regulations" might be billable for one client but non-billable for another, a distinction the software cannot make.

This manual process creates two significant problems: revenue leakage from miscategorized or missed billable hours, and high administrative overhead that pulls expensive resources away from client work. The structural issue is that existing time tracking and accounting platforms are databases with forms, not language-processing engines. They lack the ability to parse unstructured text and apply conditional business logic based on the content of a separate document like an SOW. This forces firms into a cycle of tedious, error-prone manual reconciliation.

The project would begin with a data audit. Syntora would connect to your time tracker and accounting software APIs to pull the last 12 months of time entries and invoices. This audit maps out common entry patterns and identifies the specific business logic needed to categorize time correctly against your client SOWs. You would receive a data analysis report showing exactly how the proposed AI system would handle your existing data.

The core of the solution would be a FastAPI service hosted on AWS Lambda that listens for new time entries via webhook. The Claude 3 Sonnet API would parse the entry's text description, classify it based on rules derived from SOWs stored in a Supabase database, and assign the correct billable code. Pydantic models would enforce strict data validation before the enriched data is written back to a custom field in your time tracker or directly to QuickBooks. The entire classification process would take less than 500ms.

The delivered system operates in the background, augmenting your existing tools without requiring your team to change their behavior. When it is time to invoice, your project managers would see time entries already categorized and flagged for approval in QuickBooks. A process that once took 3 hours per client could be reduced to a 5-minute review. You receive the full Python source code, a deployment runbook, and complete control over the system.