AI Nutrition Tracker
V4H2O
AI embedded in the core UX, not bolted on as a chat widget
The brief
V4H2O wanted more than a logging app, an AI layer that gives users personalized, conversational nutrition guidance grounded in their actual tracked data.
The problem
Generic chatbot integrations answer generic questions. V4H2O needed responses grounded in each user's real nutrition history, which meant a proper retrieval pipeline, not just an API call wrapped in a chat UI.
The approach
We built the full React Native app with OpenAI API integration, a RAG pipeline for context-aware responses grounded in the user's actual tracking data, real-time nutrition tracking, and a Firebase backend holding the whole system together.
Result
A conversational AI nutrition assistant where guidance is grounded in the user's real data, the model reasons over what the user actually logged, rather than answering in a vacuum.
Next project
Story Catcher →AI Video Generation Platform