Back
V4H2O
Year
2025
Tech & Technique
React Native, OpenAI API, RAG Pipeline, AI Chatbot, Firebase
Description
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.
Our 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.
The 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.
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.
Our 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.
The 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.
My Role
AI Nutrition Tracker
AI embedded in the core UX, not bolted on as a chat widget
AI embedded in the core UX, not bolted on as a chat widget