Part 3: Next-Gen Personal Running and Health Coach: Smarter AI, Deeper Insights, and Context-Aware Analytics

 Part 1 — https://medium.com/@bhattshailendra/extracting-running-data-out-of-nrc-nike-nike-run-club-using-apis-bcee76a714c3

Part 2 — https://medium.com/@bhattshailendra/from-extracting-running-data-to-transforming-it-into-ai-powered-insights-with-nike-nrc-strava-0f19742b2f11

Building on Part 2’s foundation of extracting, embedding, and querying running data from Nike NRC, Strava, Garmin, and OpenAI-powered vector search, in this part I have expanded the application to incorporate multimodal data, contextual external knowledge, and agentic tooling for more accurate AI insights.

My architecture now does the following:

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Step 1 — Multiple running, knowledge based and health data sources are ingested and normalized into the back-end.

Step 2 — Data — including multi-modal records are embedded and stored in the vector database.

Step 3 — User queries are routed through the chat app initiate vector-based similarity searches for pertinent personal data.

Step4 — The OpenAI GPT-5 LLM synthesizes responses, enhanced by injected external knowledge and agentic tool outputs.

Step 5 — Insights and predictions are returned in conversational form, evolving with feedback and new data.