I’m launching a pet-grooming site that does more than list services—I want it to think. The heart of the build is an AI engine that studies a visitor’s pet breed and instantly returns tailored grooming recommendations (coat-care routines, trim frequency, product tips, seasonal advice, and so on). I have the content ready; your job is to wrap it in logic and deliver it through a clean, mobile-first interface. Beyond the recommendations module, I also need a secure user area where owners can log in and view a timeline of every grooming visit or home session they record. Each entry should store date, notes, and any products used, so the AI’s future advice can grow smarter over time. Key deliverables • Responsive website (React, Vue, or similar) connected to a lightweight backend (Node, Django, or Laravel—your call). • AI recommendation engine trained or rule-based around breed data; open to TensorFlow, PyTorch, or a simpler ML-as-a-service approach if it speeds things up. • Pet grooming history tracking: create, edit, delete, and list past sessions per user, with persistent storage (MySQL, PostgreSQL, or MongoDB). • Admin dashboard to upload new breed profiles and tweak recommendation rules without touching code. • Deployment to a cloud host (AWS, GCP, or Vercel) with SSL, basic analytics, and unit tests for critical functions. Acceptance criteria 1. Entering “Golden Retriever” returns breed-specific grooming steps in under two seconds. 2. A logged-in user can add a grooming event, refresh the page, and still see it accurately stored. 3. Admin can update the Retriever rule set, and new advice appears live without redeploying. 4. Site scores 90+ on Google Lighthouse for mobile performance. Tell me which stack you prefer, a rough timeline, and any similar ML-driven sites you’ve shipped. I’m ready to start as soon as we agree on the plan.