I need a lean but functional mobile prototype that showcases how AI-driven product recommendations can personalize the user journey. Here’s what I’m after: • A minimal cross-platform build that runs on both iOS and Android—React Native, Flutter, or another lightweight framework is fine as long as it compiles cleanly for each store. • A simple onboarding screen to collect a few preference signals (or pull demo data). • A small, embedded machine-learning model—cloud or on-device—that returns tailored product suggestions in real time. Off-the-shelf libraries such as TensorFlow Lite, Core ML, or scikit-learn converted models are acceptable; the goal is to prove the recommendation flow works. • A basic feed or carousel UI that lists the recommended items and updates when the user changes their inputs or browsing behavior. • Clear setup instructions and commented code so I can run, retrain, or extend the model later. Keep this scoped as a proof-of-concept: no deep analytics dashboards or full checkout process at this stage, just the core recommendation experience running smoothly on both platforms.