Solutions
To build a fitness platform that truly adapts to each user, we designed the system around an AI coaching engine — not a static exercise database. The AI, powered by Google Gemini, acts as the central intelligence layer: it generates plans, interprets user data, and autonomously modifies programs week over week based on real performance, recovery, and lifestyle signals.
Key solutions
Progressive team scaling. We grew from a 4-person discovery team to a 10-person delivery squad without disrupting velocity, adapting team composition as the project evolved from proof-of-concept to full product.
Multi-agent AI architecture. We built a layered prompt system with a master persona prompt (elite fitness coach), per-feature prompts for each interaction point (meal logging, workout generation, weekly reviews), and a cross-validation layer where a secondary LLM checks outputs for safety before they reach the user.
Conversational-first UX. Instead of rigid forms, users interact with the AI through natural-language chat. They log meals by describing what they ate, reschedule workouts by saying “I’m sick today,” and adjust targets conversationally. The AI translates instructions into structured data changes in real time.
AI simulation and testing framework. We built a custom tool that generates synthetic user profiles, simulates 10 weeks of coaching interaction, then uses three competing LLMs (Claude, GPT, Gemini) to evaluate the AI coach’s decisions — surfacing errors and driving prompt improvements.
Nine-metric adaptive engine. The AI simultaneously analyzes workouts, nutrition, hydration, sleep, activity, blood biomarkers, supplements, medications, and body measurements to identify hidden barriers to progress and recalibrate the plan weekly.
Natural-language food logging. Users describe meals in plain language (“I ate borscht without meat”) and the AI estimates calories and macros — eliminating the friction of traditional database-search logging.
LLM-agnostic architecture. The system is designed so the underlying language model can be swapped via configuration, protecting against vendor lock-in and enabling rapid adoption of newer model versions.
Comprehensive compliance framework. A dedicated legal specialist created a 52-document register covering terms of use, health disclaimers, privacy policies, and AI-generated content liability across three phased launches.
The team composition for this
project features:
- 1 Project Manager
- 1 AI/ML Engineer
- 1 Backend Developer
- 3 Frontend Developers
- 1 DevOps Engineer (part-time)
- 1 UX/UI Designer
- 1 Legal Specialist
- 3 QA Engineers,
- 1 Solution Architect (part-time)
