Automate Your Firm's Time Tracking and Billing with AI

Many professional services firms rely on tools like Harvest or Toggl. These platforms are effective when consultants remember to start and stop a timer, but they fail to capture the significant amount of work that happens outside of a timer. A two-hour working session discussed in a Slack channel or a client call scheduled in Google Calendar often goes unbilled because it requires a separate, manual entry that is easily forgotten.

Then there are tools like QuickBooks Time, which integrate with accounting but enforce a rigid data structure. A consultant must manually select a customer and service item from a dropdown before logging any time. This process prevents them from adding detailed, client-facing descriptions of the work performed, leading to generic invoice line items that cause client confusion. The system requires structured input and cannot interpret the context of real-world work.

Consider a 15-person agency where time is mentioned in Slack messages, calendar invites, and project management tickets. At the end of the month, one person spends 3 full days manually collecting this information. This involves reading through hundreds of messages, asking for clarification on vague entries, and copy-pasting over 250 individual line items into QuickBooks. Last quarter, this manual process led to an 8-hour under-billing for a key client.

The structural problem is that these off-the-shelf tools are designed as digital stopwatches. Their architecture is built for manual, structured data entry, not for interpreting the unstructured language of how work actually happens. They cannot synthesize data from multiple sources into a single, billable narrative, forcing your team into tedious administrative work that costs you billable hours.

An engagement would start by auditing every place your team's work is recorded: Google Calendar, Slack channels, emails, and any project management software. The goal is to create a data map that links project names, client mentions, and task keywords to the correct billable codes in QuickBooks. This initial discovery phase produces a clear data dictionary that you would approve before any development begins.

The technical approach would use a FastAPI service running on AWS Lambda to ingest data from your tools via webhooks. When a new calendar event is created or a specific phrase is used in Slack, the service would trigger. The Claude API would parse the unstructured text to extract the client, project, task, and duration. This processed data would be stored as structured time entries in a Supabase database, creating a clean, auditable log of all work.

The delivered system connects your communication tools directly to your accounting software. The system would compile draft timesheets daily for each team member to review and approve in 5 minutes. Once approved, these entries are pushed to QuickBooks, automatically generating draft invoices ready for final review. You would receive the full Python source code, a deployment runbook, and a system that costs under $50 per month to operate.