This working paper presents a production-grade, safety-first architecture for an AI Health Coach designed to support early risk awareness using multi-source health signals. The system ingests wearable streams and structured symptom inputs, performs risk stratification using deterministic checks and ML risk bands, and grounds explanations through retrieval from curated clinical guidance (RAG). A guardrail LLM layer generates constrained, non-diagnostic explanations and next-step recommendations aligned to escalation policies. The design includes safety gates, uncertainty handling, audit logging, and traceability to enable accountability. Deployment considerations cover service orchestration, model versioning, monitoring (drift, calibration, false reassurance risk), and privacy-aware handling of health data. The goal is not to replace clinicians, but to operationalize earlier, safer decisions in real-world workflows.Published under AIInovateHub as a public architecture artifact for hiring, collaboration, and pilot discussions.
ganesh prasad bhandari (Tue,) studied this question.