AppMatic Tech builds AI coaching agents on wearable data that explain heart rate, sleep, and activity trends in plain language, using retrieval over each user's own records and permissioned MCP tools for Claude and ChatGPT. Built with Python, Node.js, and PostgreSQL in Ahmedabad, India, with audit logging.
Raw wearable numbers rarely change behaviour. AppMatic Tech builds an agent that reads a user's own trends, flags low-quality data, and explains what changed, without giving medical advice.
AI Agent Development Stack for Wearables
| Layer | What AppMatic Tech uses |
|---|---|
| Agent layer | Python and Node.js orchestration with Claude or GPT-class models |
| Data access | MCP tools scoped to the signed-in user's records |
| Storage | PostgreSQL with time-series readings and daily aggregates |
| Safety | Wellness-only guardrails and escalation to a clinician message |
| Audit | Per-call logs with retention limits |
How AppMatic Tech Delivers This
- Define which trends and questions the agent may answer.
- Build scoped tools over aggregated readings.
- Add data-quality checks so flagged readings are excluded.
- Write guardrails for wellness versus clinical statements.
- Evaluate on real user histories before release.
Frequently Asked Questions
- Does the agent give medical advice?
- AppMatic Tech limits coaching agents to wellness explanations and trend summaries, and routes anything that looks clinical to a defined escalation message.
- Which data does the agent see?
- AppMatic Tech scopes every tool to the signed-in user's own aggregated records and excludes readings flagged as low quality.
- Can assistants like Claude query the data?
- Yes. AppMatic Tech exposes permissioned MCP tools so assistants such as Claude and ChatGPT can query a user's trends with audit logging.
Further reading: Wearable Companion App Development: BLE, Health Data, and the 2026 Move to the Google Health API