Architected a preventive health system that grants users complete agency over their physical optimization. Scaled a consumer AI application that analyzes metabolic markers, entirely bypassing generic advice in favor of verified, automated clinical interventions.
Most health tracking is theater. Apps collect data but don't change behavior. Wearables log metrics that users ignore. The gap between measurement and meaningful action keeps widening.
I wanted to build something different—a system where AI doesn't just analyze your meals or labs, but actually helps you make better decisions. Not tracking for tracking's sake, but tools that improve real-world health markers.
Computer vision trained on 100K+ meals. Custom models that understand regional cuisines and portion estimation—not generic food recognition.
Systems Architecture & Data Flow
The hardest part wasn't the computer vision—it was building a pipeline that works on mobile devices with spotty connectivity. We compressed models, implemented progressive enhancement, and built offline-first sync. Results: 90%+ accuracy on meal analysis, sub-2s inference time.
Analyzed 100K+ meals with 90%+ diagnostic accuracy • Bootstrapped to $500K ARR
Scaling consumer health tech isn't about features—it's about building trust with your data. Users need to see real results, not just dashboards.
A methodology for moving AI from experiments to operating systems. Four phases, built on lessons from digital and cloud transformation waves, tested across healthcare, consumer tech, and agencies.