A fitness startup in USA approached us with an idea of developing a mobile app that can revolutionize fitness game/home workouts. The client wanted an app with cutting-edge integrations to elevate user experience. Their idea was to create an app that could simply detect the user’s pose and offer customized workout feedback. Besides, they also wanted to have a conversational chatbot built-in in the app. Their business vision was to match the trend of virtual fitness by combining real-time analysis and generative AI technologies.
Flutter
GetX
Gemini API
Google ML Kit
The client wanted to build an app that offers real-time pose detection during strenuous exercises to get the optimum benefits out of it.
Users tends to stay for a longer period if they like to get engaged or the app is worth engaging. Without much engagement or interactions, the app couldn’t survive the cut-throat competition.
The client had a demand to enable entirely new chatbot prompts and configurations without depending on constant redeployment.
Real-time user data, progress monitoring, and profile updates wanted consistent syncing across sessions and devices.
The client required a smooth pipeline to release updates quickly.
Our software development team integrated Google’s ML Kit Pose Detection API, which offers on-device machine learning for identifying body joints in real-time. This allowed the app to analyze user movements with minimal latency, delivering immediate posture correction and eliminating the need for constant internet connectivity.
With the help of Google Gemini API, our software developers implemented a chatbot powered by AI that can handle contextual conversations and deliver customized responses The chatbot would answer fitness freak questions, guide users with routines, and create with two-way fitness experience that brings back users and hook the for life.
We utilized Firebase Remote Configuration that made users believe that they can update prompts, UI content, and feature toggles directly from firebase console. As a result, the users were able to get access to full editorial control over app behavior without many changes in code.
Another solutions we offered was by integrating Firebase Cloud Firestore, which is a scalable NoSQL database that shares and stores synchronized data. There will be profile updates, preferences, and workout stats that becomes instantly available and consistent.
We enabled a robust CI/CD pipeline using GitHub Actions, which automated functions like building, testing, and deploying both Android and iOS versions. Every time with the code push, the system starts performing quality checks. As a result, it maintains high codebase and accelerate release cycles.
The project initiated after discussing and understanding the client’s vision for a real-time exercise detection mobile application with smart assistance.
Based on the requirement, flutter was finalized due to its cross-platform functionality benefit, Google ML Kit for real-time pose detection, Firebase (including Auth, Remote Config, and Firestone) for backend infra, and Google Gemini API for AI chatbot integration.
We structured pose detection mobile app using MVVM (Model-View-ViewModel) architecture that enables clean differentiation between user interfaces and logic layers. Mapped out were the user flows for profile editing, chatbot communication, exercise tracking, and login.
The app was developed under the phase that initiates with Firebase authentication, and profile management. Real-time pose detection, AI chatbot integration, and dynamic content updates followed, supported by Firestore and CI/CD setup.
Thorough testing ensured pose accuracy across body types and environments. Chatbot responses, UI elements, and app performance were refined for a seamless user experience on all devices.
The app was deployed to Android and iOS stores with automated CI/CD pipelines. Monitoring tools were used to track crashes and ensure stability post-launch.
Post-deployment, we enable Firebase Remote Config for updates to chatbot prompts and managed exercise content dynamically. The scalable architecture allows for easy expansion, including monetization, wearables, and advanced analytics.
With the app, you can receive motion tracking to uplift exercise effectiveness.
This was achieved using the interactive chatbot support driving consistent user activity.
Optimized rollout speed using Firebase Remote Config for faster iterations.
It enhances usability and responsiveness that boosts user confidence.
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