AI desktop application: AI time tracker that cuts manual timesheets
Since 2024, we have been developing an AI desktop application for TimeCamp that runs in the background, captures signals from user activity, and prepares suggested time entries for the right projects. Nothing is saved before user review.
The hard part was not capturing activity itself, but turning fragmented signals into useful time suggestions without adding another manual workflow.
Challenge
A system that could no longer keep up with growth
The product had to combine background execution, reliable activity categorization, and a predictable UX that keeps the user in control of the final timesheet.
Capturing meaningful signals from day-to-day work without adding manual overhead or friction
Turning fragmented activity data into accurate project and time suggestions
Preserving privacy and full user control in an app that runs continuously in the background
Solution
Architecture and implementation built for real operational pressure
We built the product as a desktop application with a lightweight UI, a background execution layer, and an approval flow that closes the loop between AI suggestions and final timesheet entries.
Electron-based desktop app with a React/TypeScript interface and continuous background operation
Suggestion pipeline combining activity signals with AI-assisted categorization and project assignment
Review/approve flow and release discipline across the desktop layer and native modules
Business outcome: Less manual work around time tracking, more complete timesheets, and full user control through review and approval before saving suggestions
Implementation process
From diagnosis to stable rollout
Delivery was shaped around the full chain, not just the interface: from signal capture, through activity interpretation, to safe approval and persistence of suggested entries.
Activity model and timesheet workflow
Defining which signals from user work are actually useful and how they should translate into suggested time entries.
Desktop runtime and system integrations
Building the background desktop app and the native pieces needed for stable operation at the operating system level.
AI suggestions and approval flow
Connecting activity signals with categorization logic and a review/approve step so the user stays in control before anything is saved.
Stabilization, releases, and product growth
Hardening delivery, improving suggestion quality over time, and extending the product without risking its core background workflow.
Technologies
Stack selected for the scale of the problem
The stack combined Electron, React, and TypeScript for fast UI iteration, Python for AI logic, and C++ plus Objective-C where reliable system access and background execution were required.
If the project needs to move
without chaos, start with a conversation
In a short call, we figure out whether this is even the right kind of project for us, where the biggest risk sits, and what first move creates real progress without wasting time and budget.
Close to Berlin
185 km
We are located 185 km from Berlin, one of Europe’s key business and technology hubs. That makes in-person meetings easier and collaboration in international projects more efficient.
Close to Berlin
185 km
We are located 185 km from Berlin, one of Europe’s key business and technology hubs. That makes in-person meetings easier and collaboration in international projects more efficient.
