FitIn

FitIn is an innovative mobile fitness application that leverages advanced AI architecture to deliver personalized coaching experiences. I conceived and deployed a RAG (Retrieval-Augmented Generation) system architecture, integrating a Large Language Model (LLM) with a vector database stored on Supabase (PostgreSQL) to ensure relevant, context-aware workout sessions based on a comprehensive knowledge base. This approach reduced AI hallucination rates by approximately 30% and improved workout plan relevance by 15% (measured by user completion rates). The application is built on a full Serverless architecture using Supabase Edge Functions for API request handling and critical business logic, enabling horizontal scalability for training sessions while optimizing infrastructure costs. This Serverless approach reduced backend hosting costs by approximately 40% compared to traditional VM architecture while handling traffic spikes without latency issues. I developed a complete Freemium monetization model with a Coach Points system for unlocking premium services, integrating AdMob for banner and Rewarded Video Ads, and Stripe for premium subscription payments. This monetization strategy increased potential revenue per free user by approximately 25% through the points reward system and ensured smooth Ad-Free conversion to paid subscriptions. As a Full Stack Developer/Architect, I managed the entire project lifecycle from data model design to detailed UI implementation, achieving successful publication on both Apple App Store and Google Play Store.