AI desktop application: AI time tracker that cuts manual timesheets
For TimeCamp, we develop an AI desktop app that runs in the background and reduces manual timesheet work. It prepares suggested time entries for the right projects while the user stays in control and approves everything before saving.
The key was turning fragmented work signals into useful time suggestions without adding another workflow and without taking control away from the user.
Challenge
A system that could no longer keep up with growth
The product had to combine background execution, reliable activity categorization and a simple approval flow where the user still decides what gets saved.
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
The work covered the full chain: capturing work signals, interpreting activity, approving suggestions safely and saving the final time entry to the right project.
Activity model and timesheet workflow
Defining which work signals are 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
Stabilizing delivery, improving suggestion quality, 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.
Have a system, product or business area you want to build or develop?
You do not need a finished specification. A problem, idea, or direction to validate is enough. We will talk through the goal, constraints, and the first step that makes sense commercially and technically.
How we start
24h
After your message, we reply with a call slot and an initial assessment. We will help decide whether to build, integrate, automate, or start simpler.
How we start
24h
After your message, we reply with a call slot and an initial assessment. We will help decide whether to build, integrate, automate, or start simpler.
