Challenge
Working with time tracking data through AI assistants meant waiting on repeated API calls every time a report or entry was needed. There was no way to get enriched context like project names and client details alongside raw time entries without writing custom scripts.
Solution
84EM extended an existing open-source integration with an intelligent caching layer that keeps time data ready without hammering the API. Teams can now pull reports, start timers, and review billable hours through conversation with full project and client context attached to every entry.
Technical Implementation
- 95% cache hit rate so repeat queries return instantly instead of waiting on network round-trips.
- Automatic data enrichment that attaches project names, client details, and workspace context to every time entry.
- Daily and weekly reporting that breaks down hours by project, client, and workspace without manual exports.
- 22 tools covering time entries, reporting, project management, and cache operations.
- Flexible date filtering so teams can query any time range without building custom reports.
- Cache management controls that let teams warm, inspect, and clear cached data on demand.
Results
Teams can review time data, generate reports, and manage entries without leaving their AI workflow or waiting on slow API responses. The project shipped as open-source under the GPL-3.0 license, building on community work and free for any team using Toggl to adopt.
