In today's digital landscape, personalization is key to enhancing user experience and engagement. AI-driven personalization engines have transformed the way businesses interact with customers by analyzing user behavior and preferences to deliver tailored experiences, which enhance engagement, increase conversion rates, and foster customer loyalty. These systems process vast amounts of data in real time, allowing businesses to adapt to user needs promptly. The AI-driven personalization engine follows a streamlined four-step workflow: data collection, user profiling, pattern analysis, and recommendations generation. Recommender systems are sophisticated tools designed to suggest products or content to users based on their preferences and behaviors, and there are two primary types of recommender systems: collaborative filtering and content-based filtering. Hybrid approaches combine the strengths of both methods, knowledge-based systems utilize explicit knowledge about users and items, context-aware recommendation systems consider contextual information, real-time personalization is crucial for delivering timely and relevant recommendations, and Shaped.ai is a tool designed to simplify the integration of AI-driven recommendations into applications. To effectively implement a recommendation engine, businesses need to set up the environment by creating a SingleStore account, workspace, and database, ingest data, train the recommender model using Shape.ai, and deploy real-time recommendations in action.