Enhance Shopping App With AI

My e-commerce app is already in development, but I want to take it several steps further. The first priority is adding an AI engine that serves truly personalized product recommendations. I want the system to draw on three specific data sources—each of which already exists in the project’s database layer:

• past purchases and browsing history
• products a shopper has saved or added to cart
• answers to a short preference quiz that appears during onboarding

Those signals should be fed into a recommendation model (I’m open to TensorFlow, PyTorch, or a SaaS ML API if you can justify the choice) and surfaced in real time throughout the customer journey—especially the home screen and product pages.

Alongside the AI work, I need the front-end modernized so the new recommendations slot in seamlessly with a clean, responsive UI. The current stack is React Native for mobile and Node/Express for the back end; everything is in a private GitHub repo and runs in Docker-based staging.

Deliverables I will review before sign-off:
• Integrated AI recommendation module with documented training pipeline or API usage
• Updated UI components showing live, personalized picks
• End-to-end test cases proving recommendations change when the underlying data changes
• Brief hand-off doc outlining model retraining or parameter tuning procedures

If you have proven experience merging AI with commerce apps and can work within my existing stack, let’s talk timeline and milestones right away.