A hybrid mobile/web application powered by a proprietary computer vision model achieving 99%+ accuracy—from AI training to cross-platform deployment.
A company with an innovative physical product needed a sophisticated digital companion to complete its ecosystem. The vision was to create a hybrid mobile and web application powered by a custom-trained deep learning model.
This application would analyze user-submitted images, identify specific biomechanical patterns, and in response, unlock a personalized library of therapeutic video content, creating a deeply engaging and customized user journey.
This was a greenfield project of immense complexity. The challenges were threefold:
AI Model Development: We had to train, test, and deploy a proprietary computer vision model (YOLOv11) capable of analyzing images with over 99% accuracy.
Hybrid Application Architecture: The solution required both a native-performing mobile app (iOS/Android) for existing customers and a web version for direct traffic campaigns, all sharing a unified codebase.
End-to-End System Integration: The project demanded the seamless integration of the AI model, user authentication, a NoSQL database, a content management API, and adaptive video streaming services into a single, cohesive user experience.
I engineered a multi-layered, cloud-native platform from absolute zero.
The user-facing application was built as a hybrid solution using Angular 20 and Ionic 8, allowing for a single TypeScript codebase to be deployed as a native mobile app (via Capacitor 7) and a progressive web app (PWA). This strategic choice drastically reduced development time and long-term maintenance costs.
The core innovation lies in the AI pipeline. User images are securely uploaded to a custom AI analysis API, deployed on Google Cloud Run. This serverless endpoint runs a highly optimized PyTorch model that performs the biomechanical analysis. The API was engineered for high availability, featuring intelligent retries, cold start detection, and dynamic timeouts to ensure a smooth user experience even under heavy load.
User data, analysis results, and content metadata are managed through Google Firebase, utilizing Firestore for a real-time NoSQL database and Firebase Authentication for secure social (Google OAuth) and email/password login.
Ionic/Angular
iOS/Android/WebAuth & Firestore
User DataGoogle Cloud Run
PyTorch ModelHeroku
Video LibraryA custom-trained deep learning model that analyzes user-submitted images to detect complex biomechanical patterns with over 99% accuracy. The system provides users with an instant, detailed breakdown of its findings.
Based on the AI analysis results, the app unlocks a curated library of therapeutic and educational videos. The content is organized into series and tagged by topic, with adaptive streaming via HLS.js ensuring high-quality playback.
A single, modern TypeScript codebase powers a native-performing iOS & Android app (via Capacitor) and a fully functional web app. This approach ensures feature parity and simplifies future updates.
The entire system leverages a robust, scalable backend using Google Firebase for user management and a serverless Google Cloud Run instance for the AI. This ensures data security, real-time synchronization, and cost-effective, automatic scaling.
Single TypeScript codebase for iOS, Android, and Web
Custom-trained computer vision model with 99%+ accuracy
Serverless AI API with automatic scaling and cold start optimization
Firestore NoSQL database + OAuth & email authentication
Video content metadata and organization system
Adaptive bitrate streaming for optimal video quality
This project represents a true end-to-end product development cycle: from foundational AI research and model training to building a polished, scalable, and secure consumer-facing application. It delivered a powerful digital companion that dramatically enhances the value of the client's physical product.
This project involves proprietary artificial intelligence and critical business logic for a leading company. To protect our client's intellectual property and competitive advantage, the specific biomechanical patterns analyzed, the full user flow, and detailed datasets have been omitted from this case study.
From training a custom deep learning model to engineering the cross-platform application that leverages it, this project showcases our capability to deliver deeply complex, AI-driven solutions from concept to launch.
Have an ambitious AI project? Let's talk.