Improve Time Tracking Accuracy with Custom AI

Most professional services firms use tools like Harvest or QuickBooks Time for logging hours. These platforms are effective digital stopwatches but rely entirely on manual entry. An employee can log '6 hours - Q3 Planning' and the system accepts it without context. There is no automated link between that entry and the actual work done, such as the 3 hours of documented meetings in Google Calendar or the 50 emails exchanged on the topic.

A typical scenario involves a 15-person agency spending two full days at the end of each month just to validate timesheets. A project manager manually compares timesheet reports against Slack conversations, Asana task updates, and Google Docs version history. This process creates a 3-5 day delay in sending invoices and is filled with subjective questions for the team, slowing everyone down.

The structural problem is that time tracking tools are built as simple data recorders, not analytical systems. They are architected to store records containing a user, a project, and a duration. They cannot ingest and interpret the unstructured text from an email thread or a meeting invitation to automatically suggest that a 1-hour block should be billed to a specific client. This architectural limitation forces firms to perform that costly analytical work by hand.

The first step is an audit of your data sources. Syntora would map where all project-related activity occurs: Google Workspace for meetings and emails, a project management tool like Asana for tasks, and your accounting platform for existing client data. This audit defines the required data pipelines and identifies the unique vocabulary your team uses to describe work, like specific project codes or client shorthand.

The technical approach would use AWS Lambda functions to pull data from these sources every 15 minutes. This data feeds into a central FastAPI service where the Claude API parses the unstructured text from each activity. The Claude API excels at named entity recognition, reliably extracting client names, project IDs, and task descriptions from messy text. This structured output is then stored in a Supabase database, creating a verifiable, minute-by-minute log of all project activity.

The delivered system would be an internal dashboard that compares logged hours from your timesheet software against the AI-generated activity log. The interface highlights discrepancies, such as an 8-hour timesheet entry with only 3 hours of detectable activity, for manager review. This provides a fast, data-backed validation layer that integrates with your existing tools, rather than forcing you to replace them.