1. AI & Computer Vision Integration (TensorFlow, OpenCV): The application leverages TensorFlow and OpenCV for real-time video analysis, using computer vision to track body movements and compare them against trained models of correct workout form. This allows the app to deliver instant, personalized feedback to users, ensuring optimal technique and reducing the risk of injury during workouts.

2. Scalable Backend & Real-Time Data Handling (Node.js, Python, Redis): Node.js handles API requests and user sessions, while Python is used for AI model inference. Redis is employed as a real-time cache for workout session data, ensuring fast feedback to users. This setup supports multiple simultaneous users with low latency, providing a seamless experience even in high-demand scenarios.

3. Cloud Infrastructure & Data Storage (AWS, Lambda, S3): ActiveMentor is built on AWS, using Lambda for scalable serverless computing to handle real-time model inference and video processing. User data, including videos and workout history, is securely stored in S3 buckets, allowing for efficient access and retrieval, while ensuring the application scales effortlessly with user growth.