AI-Led Connected Health PlatformAI-Led Connected Health Platform

The client is a US-based digital health company operating across MedTech, remote monitoring, and preventive care. Their platform connects individuals, clinicians, insurers, and healthcare teams through wearable-enabled monitoring, licensed provider engagement, and digital care workflows.

Digital health platforms reach a commercial ceiling when their AI capabilities, mobile reliability, and clinical credibility haven't kept pace with their growth ambitions. The client had a health ecosystem spanning wearables, apps, provider tools, and care workflows. But the path to enterprise health system partnerships and clinical trial use cases ran directly through closing those gaps.

The client needed to solve three product-critical challenges to scale their platform:

1. A health coaching assistant POC needed to move into a production-ready system capable of performing under clinical scrutiny.
2. Persistent instability across iOS and Android was creating friction across core user journeys, wearable connectivity, and data reliability, undermining confidence in the platform at scale.
3. The platform needed stronger AI and ML interpretation to convert physiological signals into insights on stress, strain, recovery, and health risk.

Moving a health platform from technically functional to enterprise-ready requires the architectural depth to rebuild AI systems that can withstand clinical and procurement scrutiny, and the delivery discipline without destabilizing a live product.

Coditas embedded into the client's engineering function, bringing AI system architecture, mobile engineering, and backend reliability under a single, governed engagement aligned with their sprint cadence, repositories, and release processes. The client trusted our AI-Augmented Engineering approach: rebuilding a production AI system with the observability, performance controls, and human-in-the-loop design that clinical-grade deployment demands.

We re-engineered the client's health coaching assistant into a production-ready AI backend with intent classification, RAG-based retrieval over a structured health knowledge base, health-context handling, voice-based activity logging, personalized responses, daily health briefs, and clinician-facing reporting support. AI-generated outputs are now structured for clinician review and escalation rather than autonomous delivery, keeping human oversight built into the system where it matters most.

Coditas also stabilized the mobile product foundation across iOS and Android, resolving a 900+ ticket backlog, improving wearable connectivity, and strengthening health data reliability across the sleep, steps, heart rate, HRV, blood pressure, SpO2, weight, and glucose modules.

iOS, Android, Google Gemini, RAG-based Knowledge Retrieval, Intent Classification, Voice-based Activity Logging, Wearable Data Integration, Caching, Async Parallelization, AI Observability, HIPAA-compliant Cloud Infrastructure

Our solution helped improve product quality, AI performance, user experience, and platform readiness across the client's connected health ecosystem.

• Reduced AI response time from 10 seconds to <3 seconds
• Reduced model token usage by roughly 90% through intelligent fact filtering and caching
• Resolved a 900+ ticket Android backlog
• Delivered a production-ready, patient-facing AI coaching system for enterprise health system demos
• Opened new enterprise and clinical trial use cases through voice coaching, personalized health briefs, and clinician-facing reporting
• Positioned the platform for health system partnerships requiring demonstrable, production-ready AI capability

In healthcare, AI earns clinical trust the same way clinical tools do — through demonstrated reliability, measurable performance, and human oversight built into the system from the start. The organizations that reach enterprise health system conversations are the ones that treat production-readiness as a design requirement, not an afterthought. This engagement shows how healthcare AI becomes valuable when reliable engineering, governed workflows, and usable experiences work together.