Business type
Evidence-based nutrition education and coaching product
All case studies
Nutrition AI · WhatsApp Product
Atleet Buddy WhatsApp Nutrition Assistant
An interactive WhatsApp AI assistant for I'm a Foodie readers, designed to turn the Eet als een atleet book into a practical nutrition support experience with grounded answers, clear AI boundaries, credit-based usage, subscriptions, and admin oversight.
Core problem
Readers needed practical, fast answers from book content while the team needed reliability, credits, subscriptions, and oversight
Main outcome
A more stable WhatsApp AI assistant experience grounded in book content with stronger product controls
Business impact
Strengthened product readiness by improving assistant reliability, usage visibility, subscription awareness, and admin controls for a WhatsApp-first nutrition product.
Safer answer behavior
Usage visibility
Admin control layer
The challenge
The business friction
The product needed to transform a trusted book into an interactive assistant without becoming a vague health chatbot. Sports nutrition questions can be personal, nuanced, and sensitive, so the assistant needed to stay grounded in the source material, avoid medical advice, handle follow-up questions, maintain consistent language, support trial and paid usage, and give admins visibility into users, credits, subscriptions, usage, and estimated AI costs.
The solution
The system response
MASK AI improved the assistant behavior and the product layer around it. The work covered answer quality, retrieval context, fallback logic, refusal behavior, multi-turn conversation handling, and language consistency, plus the commercial infrastructure around trial access, prepaid credits, subscriptions, usage tracking, cost visibility, and admin controls.
Capability map
What the system covers
Built around the operational jobs that matter: intake, product experience, workflow control, visibility, and follow-through.
WhatsApp-based AI assistant experience
Book-grounded answer behavior
Clear fallback and medical-safety boundaries
Multi-turn follow-up support
Trial, credit, and subscription visibility
User and plan management
Usage analytics and estimated AI cost tracking
Admin dashboard for product oversight
Admin controls and support visibility
Foundation for a commercially managed AI product
Operating path
How the system flows
Nutrition assistant loop
WhatsApp questions move through grounded context, safety checks, answers, credits, and admin review.
08 stages
01
WhatsApp Question
02
Book-Grounded Context
03
AI Assistant Logic
04
Safety and Scope Check
05
Answer Delivery
06
Credit Usage
07
Admin Dashboard
08
Support Review
Technology
Stack and architecture
The engagement combined product interface work, workflow logic, and implementation tooling around the operational needs of the project.
- LLMs
- React
- Supabase
- Dashboard UI
- Usage Analytics
- Role-Based Access
