Helio — The AI-Powered Wellness Companion That Users Actually Stick With
We designed and engineered a cross-platform health tracking app from zero — wearable sync, an on-device AI coaching engine, and deeply personalised insights that turned passive data collection into real behaviour change.
Turning health data into habits that stick
The wellness app market is saturated with step counters and calorie loggers that users abandon within two weeks. Helio Health came to us with a different thesis: the problem isn't data collection, it's that apps never translate raw numbers into actionable, personalised guidance that adapts to your actual life.
They wanted a cross-platform mobile app that could sync with any major wearable — Apple Watch, Garmin, Fitbit, Whoop — aggregate biometric data in real time, and use an on-device AI coaching layer to surface exactly the right insight at the right moment. Not a generic notification. A genuinely personal recommendation grounded in months of the user's own data.
Eighteen weeks after kick-off, Helio launched on the App Store and Google Play. Within six months it had crossed 100,000 active users with a day-30 retention rate of 68% — more than double the wellness app category average.
Existing health apps collected data but didn't generate understanding. Users had step counts, sleep scores, and heart-rate graphs with no context for what any of it meant for them specifically. The result was novelty-driven engagement that collapsed after the first week, with day-30 retention across the category sitting below 30%.
Building AI that feels personal, not creepy
The technical and product challenges here were intertwined. On the technical side, wearable APIs are notoriously inconsistent — each vendor has its own data model, sync frequency, and permission model. Normalising heart-rate variability, sleep stage, and activity data across five different device ecosystems into a coherent unified schema was a significant engineering effort in itself.
The AI coaching engine presented a different kind of challenge. Health data is deeply personal, and users are sensitive to feeling surveilled. The coaching had to feel genuinely helpful rather than algorithmic. That meant running as much of the inference as possible on-device, keeping the conversational model grounded strictly in the user's own longitudinal data, and giving users full transparency and control over what data was used.
Privacy regulation added a third layer. HIPAA-adjacent data handling requirements, GDPR for European users, and App Store guidelines around health data all constrained the architecture in ways that had to be designed for upfront.
- Five wearable ecosystems with incompatible data models and sync APIs
- On-device ML inference required — health data cannot leave the device by default
- HIPAA-adjacent data handling and GDPR compliance from day one
- Sub-100ms UI response time for real-time metric updates
- Offline-first — users must see their data without a network connection
- iOS and Android parity with a single codebase and no compromises on feel
- Unified biometric schema with an adapter layer per wearable SDK
- Core ML (iOS) and TensorFlow Lite (Android) for on-device inference
- End-to-end encryption with user-held keys; server stores only ciphertext
- React Native with Reanimated 3 for 60fps animations on both platforms
- WatermelonDB for offline-first local storage with background sync
- Longitudinal personalisation model trained per-user on-device over 14 days
A three-layer architecture: sync, store, coach
We designed Helio around three distinct layers that each had to work independently and seamlessly together. The sync layer handles all wearable communication — polling, webhook reception, conflict resolution, and normalisation into Helio's canonical biometric schema. It runs as a background task and keeps working when the app is closed.
The storage layer uses WatermelonDB, a high-performance SQLite-backed database purpose-built for React Native, giving users instant access to months of historical data with zero network dependency. Encrypted syncing to the cloud backend happens opportunistically, ensuring data is never lost even if a device is replaced.
The AI coaching layer runs a fine-tuned language model compressed to run on-device via Core ML and TensorFlow Lite. It is grounded by a retrieval system that surfaces relevant slices of the user's own historical data as context, so every response is specific to that person's patterns — not generic wellness advice.
We chose React Native over Flutter for this project because of the depth of its native module ecosystem for wearable SDKs and HealthKit. The trade-off is slightly more bridging code, but it gave us direct access to every wearable API without waiting for community plugins. Reanimated 3 closed the animation gap entirely — users on both platforms consistently describe the app as feeling "native."
18 weeks to dual-platform launch
We ran Helio in five phases. Given the AI coaching component, we front-loaded the data and ML work so we had real on-device inference running early — not as a last-week feature bolt-on.
Chosen for performance, privacy, and platform depth
What actually changed for users
Six months after launch
The quality of code and architecture they delivered was beyond what we expected. We've scaled to 100k users without a single major incident. What impressed me most was how they handled the AI coaching component — they pushed back on our initial idea to send all data to the cloud and proposed the on-device approach instead. That decision is now our biggest product differentiator and our strongest privacy story. That's what a genuine engineering partner looks like.