Brain Changer - ML Driven Wellness App
Key Results & Impact
- Over 2000+ active users globally
- 85% user-reported improvement in mental well-being
- Featured in major health and technology publications
- Integration with popular health tracking platforms
The Challenge
Users needed a personalized mental wellness solution that could adapt to their unique patterns and provide evidence-based interventions.
Our Solution
Created an ML-powered wellness app that learns from user behavior and biometric data to deliver personalized meditation, exercises, and cognitive enhancement activities.
Technologies Used
- Python
- Scikit-learn
- Flutter
- Google Cloud AI
- TensorFlow
- iOS
- Android
How it works
The app is written in Flutter, so iOS and Android share one codebase. It collects two kinds of signal: what the user does in the app (which sessions they start, finish or abandon, and at what time of day) and, with permission, data from connected health tracking platforms.
Those signals feed recommendation models built with Scikit-learn and TensorFlow and served from Google Cloud. The output is a ranked list of sessions for that person at that moment. Short breathing exercises in the morning for one user, longer guided sessions in the evening for another.
The hard part
A new user has no history, so the first week is the hardest to personalize. The app starts from a short onboarding questionnaire and sensible defaults, then shifts weight to observed behavior as it accumulates. Getting that handover right mattered more than model sophistication.
Health-related data raises the bar on privacy. Collection is opt-in per source, and the models work from derived features instead of raw records where possible.
This project is part of our AI Agent Development work.
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