Savage Mushroom App Development Case Study

How M TECHUB LLC designed and developed an AI fitness companion with smart meal planning, adaptive workouts, real-time progress tracking, and a medically grounded calorie engine.

AI Meal PlanningWorkout ProgrammingCalorie EngineeringProgress AnalyticsOnboarding UXApp Store Launch

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App Store

Approved

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The Scope

Project Overview

AIVATAR PTY LTD wanted to build an AI-powered fitness companion that creates personalized workout plans, AI-generated meal programs, calorie targets, and progress tracking without forcing users to manually count every meal or hire a personal trainer. M TECHUB LLC handled product strategy, UI/UX design, React Native mobile app development, backend architecture, Gemini AI integration, QA testing, and app launch support for this Savage Mushroom app development project.

Project Details

Industry

Health & Fitness

Platform

iOS, Android, Admin Web Panel

Timeline

6+ Months

Team Size

8 Specialists

Location

Australia

Tech Stack

React Native, Node.js, PostgreSQL

The Challenge

The main challenge was to build an AI fitness system that feels simple for users but runs on medically sound, deterministic logic.

Deterministic Calorie Engine

Computing precise, medically valid calorie targets from 67 onboarding data points without approximation.

AI Meal Plan Generation

Producing personalized weekly meal plans via Gemini AI with tool-calling image sourcing at scale.

Adaptive Workout Programming

Adjusting exercise selection, volume, and intensity dynamically based on each user’s goals and progress.

Frictionless 67-Point Onboarding

Designing a 15-screen onboarding flow that collects deep user data without creating drop-off or friction.

Apple App Store Compliance

Navigating Apple’s review process for AI health apps, clearing all guidelines on first resubmission.

Cross-Platform Consistency

Delivering a seamless experience across iOS and Android from a single React Native codebase.

Real-Time Progress Analytics

Surfacing meaningful fitness insights and trend data without overwhelming users with raw numbers.

Image Sourcing at Scale

3-tier fallback strategy (Spoonacular → Edamam → Pexels) ensuring rich visuals across all meal screens.

Our Solution

M TECHUB LLC engineered a complete AI fitness ecosystem focused on personalization, scientific accuracy, and beautiful UX.

01 67-Point Onboarding System

02 Deterministic Calorie Engine

03 AI Meal Plan Generator

04 Adaptive Workout Engine

05 Real-Time Progress Dashboard

06 3-Tier Image Sourcing

07 Cross-Platform React Native App

08 App Store & Google Play Launch

Key Features We Built

Engineered for performance, built for real-world impact.

1 User Onboarding Flow

67-point data collection across 15 screens with AI avatar guide, biometric inputs, and goal mapping.

2 AI Meal Plan Dashboard

Weekly personalized meal plans by Gemini AI with zero tracking required and auto-sourced food imagery.

3 Adaptive Workout Engine

Customized routines with exercise animations, set/rep tracking, and real-time rest timers.

4 Progress & Reports

Daily and weekly performance reports with macro rings, calorie tracking, and visual trend data.

5 Ingredient & Recipe Detail

Macro-accurate ingredient lists with prep steps, portion sizes, and dietary swap suggestions.

6 Calorie Engineering System

Medically grounded calorie and macro targets validated against certified nutritionist benchmarks.

7 Push Notifications

Smart daily reminders for meals, workouts, and hydration with motivational AI-generated messages.

8 Admin Dashboard

Backend panel for managing users, monitoring analytics, and controlling AI plan parameters.

User Experience Design

The design focused on motivation, clarity, and trust. Since fitness users need confidence in their plan, every screen was built with visible progress indicators, bold brand energy, and a dark-first interface to reduce eye strain during workouts.

Technical Architecture

Mobile App

Backend API

Database

Payment Gateway

Our Development Process

1 Discovery & Conceptualization

Understanding AIVATAR’s vision and target fitness audience across diverse goals and body types.

2 Market Analysis

Gauging the potential and gaps in AI-powered fitness applications across the global health tech market.

3 Technology Selection

Choosing Gemini AI, React Native, Node.js, and PostgreSQL for scalable, intelligent fitness implementation.

4 AI Engine Development

Building the deterministic calorie engine, AI meal plan generator, and adaptive workout programming system.

5 Design & Development

Creating intuitive onboarding flows, interactive fitness dashboards, and cross-platform UI across web and mobile.

6 Testing, Submission & Launch

Rigorous testing of calorie logic, API responses, and App Store compliance. Deployment and post-launch support.

Project Outcome

Impact Highlights

24+

Weeks Timeline

67 App Screens

8+

Core Modules

Dual

iOS + Android

1 Admin Panel

94%

Secure APIs

Want to Build an AI Fitness App Like This?

M TECHUB LLC can help you plan, design, develop, and launch an AI-powered fitness mobile app, health platform, or personalized nutrition system.

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