// CASE_STUDY_01
SOLAR VISION
ROLE
Lead Engineer
STACK
Tensorflow, Python
YEAR
2025
OUTPUT
Mobile App (iOS)
THE CHALLENGE
Human metabolism is a complex variable. Traditional tracking apps rely on manual input, which introduces
a 40% margin of error.
**Solar Vision** was architected to eliminate human error. Using computer vision models trained on
50,000 food datasets, the engine identifies caloric density from a single photo with 98% accuracy.
THE ARCHITECTURE
The backend runs on a distributed node network tailored for high-concurrency image processing. We utilized on-device edge computing to reduce server latency, delivering analysis results in under 200ms.