Longevity Copilot Sentra

Preventive health technologies now generate more data than ever. Wearables, biomarker tests, and health apps promise deeper insight, but more information rarely leads to better decisions.

This project explores how AI can turn fragmented health data into clear, actionable guidance.

This case study is the result of the "AI for Designers" Bootcamp by Patricia Reiners. It teaches designers how to meaningfully integrate AI into the full design process: Research, ideation, prototyping, iteration, and workflow.

Explore Sentra

Observed Problem

Health-conscious adults seeking to prevent decline and maintain function are poorly served by today's healthcare landscape. Medical checkups use generic ranges and episodic care, while consumer tools produce overwhelming data without prioritization. Users spend time and money on tests and interventions but remain uncertain whether their actions meaningfully improve health outcomes.

Concept

An AI health copilot named Sentra. It interprets personal data and surfaces the single most relevant insight at any moment. It's the single source of truth for several consumer tools and medical data. Sentra replaces dashboards with prioritized guidance, showing why it matters and whether action is needed.

Feature List

- API to common consumer tools

- Upload for medial data i.e. lab results

- Primary focus + confidence, dependent on existing data

- Commitment for improvement, if needed

- Track all data in smart clusters

- Bridge to physicians: preparations for the next visit

- Control over data, anytime & always

- Trust & Ethics: Boundaries of AI well communicated

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