
Performance and supplement guidance shaped around the athlete, not a generic template.
SportiveAI is designed as a mobile-first recommendation system for youth athletes. It takes structured athlete inputs, applies a safety-first recommendation engine, and returns a clear supplement stack with timing guidance that can work for individuals and academy environments alike.
Deep AI Analysis
Analyzing athlete profile
Biometric Scan
Analyzing athlete profile
Athletic Analysis
Processing training data
Health Assessment
Evaluating health metrics
Database Search
Searching 58,320 protocols
1147 protocols
Protocol Match
Generating recommendation
Match Found!
Creatine (1g), Beta-Alanine (500mg), Multivitamin
Personalized for Sofia M.
Matching Formulation
Assembling recommendation
Generating final stack
Personalized Recommendation
Final protocol output
Personalized Recommendation
Performance Stack
Creatine (1g), Beta-Alanine (500mg), Multivitamin
Balanced meals, consistent sleep, and hydration improve performance.
Dosage
1x/day each
Timing
Creatine and Beta-Alanine post-training, vitamins in the morning
Personalized for Sofia M. based on age group, combat sport profile, performance goal, and dietary context.
Analysis Progress
Product Summary
- -The experience starts from athlete context rather than a fixed supplement template.
- -Recommendation logic is framed as a visible system with inputs, safety processing, and a usable output layer.
- -The product language stays practical and trustworthy instead of feeling experimental.
Core Components
- -Athlete profile intake covering age, sport, training load, goals, allergies, and fatigue
- -Rule-based safety logic for contraindications, dosage boundaries, and timing checks
- -AI-assisted recommendation layer that turns inputs into usable daily guidance
- -Mobile product surface for repeat use by athletes, parents, and staff
Why It Matters
- -Personal guidance with clear operational safeguards
- -Credible nutrition workflow for academy-scale environments
- -Readable outputs instead of opaque recommendation logic



