Helio Health — 24/7 care in eleven languages
Helio's call-center was the bottleneck: 14-minute hold times, six languages supported by human staff, no after-hours coverage. We shipped two native apps with an on-device voice interface and a fine-tuned medical chatbot — same clinical guardrails, eleven languages, zero hold time.
This brand is illustrative — not a paying client. The architecture, stack, and approach below are the real playbook we'd use to ship this engagement. Named-client case studies replace these as paying engagements ship.
What we walked into.
Helio served patients across South Asia and the Middle East with a five-language call center that couldn't scale. Compliance and clinical accuracy were non-negotiable. Existing chatbot vendors couldn't handle code-switching or low-resource languages — and couldn't be self-hosted.
How we built it.
- 01
Built native iOS (SwiftUI) and Android (Jetpack Compose) apps in parallel, sharing a small Kotlin Multiplatform layer for the audio pipeline
- 02
Fine-tuned a 70B medical model on Helio's clinical guidelines + 18 months of de-identified support transcripts
- 03
Shipped on-device whisper-style speech-to-text so consultations work offline and audio never leaves the patient's phone
- 04
Streamed responses with sub-second first-token latency by routing through Vercel Edge functions colocated with the user
- 05
Implemented a clinician escalation handoff — if the model isn't 95% confident, a human picks up mid-conversation with full context
What changed for the business.
50,000 MAU within six months; 4.8★ on both stores
Support costs reduced 42% in the first quarter after launch
Languages expanded from 5 → 11, including Sinhala, Pashto, and Bengali dialects
P95 latency held at 320 ms across regions thanks to edge-routed inference
Built with.
"The app handles 70% of the conversations our call-center used to take. The other 30% reach a clinician with the full transcript already summarized. It's a different operation."
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