THE AI ECOSYSTEM Bridging RAG (Retrieval-Augmented Generation) with User Centricity.

Overview of Foodhak Assistant / AI Chatbot • Frameworks • Agents Orchestration • Logic: Insights (Reward)

The Problem

  1. Users felt overwhelmed by generic health advice
  2. Overwhelming food choices / unaware of nutrients needed
  3. Exhaustion on downloading too much apps
  4. Another Chatbot feature

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Chatbot experimental feature 1.0

The Idea

We aim to designed a "lifestyle companion" AI product. Instead of a blank chat box, we’ve implemented Contextual Triggers the AI proactively surfaces insights based on wearable data (e.g., "Your glucose peaked after lunch; here's why…"). (”You should reduce your sodium intake since you’ve been munching on XYZ past 3 days”). (”If you walk an extra 10 mins today, we could reduce X weight on weight day…”).

  1. We needed to transform static health data into actionable, safe, and personalized dialogue.
  2. Be insightful, hyper-personalised health advice or actions - find ways to streamline to their lifestyle, enable delights and optimism.

High Level Technical Deep-Dive

  1. Our AI chatbot was built on a RAG architecture. This ensures that the provided responses cited is clinically-backed and personalised to user. Thus, eliminating the risk of inaccurate responses and recommendations, building user’s trust.
  2. Using 3rd party *Terra API for activities tracking, syncing major apps like (fitbit, strava, apple, google tracking)
  3. *Hyper-localisation and real-time RAG for precise information and immersive experience.

The Impact

  1. +40% increase in weekly active users (WAU) interacting with the Assistant. especially on meals logging feature.
  2. User also recur due to its anticipating daily insight. (e.g., sleeps and meal intake analytics of yesterday and week’s summary)

Setbacks