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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.

  • LLMs
  • WhatsApp
  • React
  • Supabase
  • Dashboard UI
+2 more
01
Business type

Evidence-based nutrition education and coaching product

02
Core problem

Readers needed practical, fast answers from book content while the team needed reliability, credits, subscriptions, and oversight

03
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.

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.

01

WhatsApp-based AI assistant experience

02

Book-grounded answer behavior

03

Clear fallback and medical-safety boundaries

04

Multi-turn follow-up support

05

Trial, credit, and subscription visibility

06

User and plan management

07

Usage analytics and estimated AI cost tracking

08

Admin dashboard for product oversight

09

Admin controls and support visibility

10

Foundation for a commercially managed AI product

Operating path

How the system flows

WhatsApp questions move through grounded context, safety checks, answers, credits, and admin review.

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
  • WhatsApp
  • React
  • Supabase
  • Dashboard UI
  • Usage Analytics
  • Role-Based Access
Outcome

What changed

The project helped Atleet Buddy operate as a more managed commercial AI product: clearer for readers, safer in scope, easier to monetize, and easier for the team to monitor as usage grows.

Safer answer behavior
Usage visibility
Admin control layer
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MASK AIMASKAI

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