FitLife: a diabetes-aware AI fitness platform with personalised meal and workout plans

The bottleneck

Off-the-shelf fitness apps are generic by design. Same workout schedule, same meal plan, lightly tailored to weight and goal. For the average user that\'s mildly unhelpful; for someone managing type 1 or type 2 diabetes it can be actively unsafe.

Glucose response to specific foods is individual. Workout intensity needs to factor insulin timing. Meal plans need accurate carb counts. None of those constraints fit a generic recommender that averages every user down to "moderate intensity, balanced macros".

What I built

A bespoke Next.js platform built around the personalisation engine:

• Onboarding capture. Profile, dietary preferences, medications, fitness baseline, and parsed consultation notes from existing professional sessions.
• AI personalisation engine. Anthropic Claude takes the user\'s full context — onboarding, preferences, consultation notes, recent glucose trends — and generates meal plans with carb counts and macros plus workout plans with progressive overload calibrated to the user\'s fitness level. Latency target: meals under 10 seconds, workouts under 5.
• Glucose + insulin tracking. First-class data primitives, not afterthoughts. Surfaces in the dashboard, feeds the personalisation engine\'s context window.
• Surrounding modules. Member community, science articles, consultations, admin tooling, preferences, profile, wellbeing tracking, test-results capture.

What changed

A diabetes-aware fitness platform that respects individual physiology rather than averaging it away. The Claude integration delivers meal plans in under 10 seconds and workouts in under 5 — fast enough to be usable in the moment a user opens the app, not a daily batch they wait on.

The platform continues to extend — glucose pattern detection, insulin timing nudges, and tighter consultation-notes integration are the next load-bearing capabilities being shaped.