I want to bring an AI-driven marketing platform from concept to a working MVP. The core engine must deliver three things out of the box: • Accurate customer segmentation that can cluster audiences in real time from first-party and third-party data • On-the-fly personalized content generation for ads, email, and landing pages • Predictive analytics that surface next-best-action insights and revenue forecasts While e-commerce, finance, and healthcare will certainly benefit, my vision is broader; I want the architecture to stay flexible enough for any brand, agency, Meta campaign manager, SEO specialist, or in-house growth team to plug in and start seeing lift. Here is how I picture the initial scope: 1. Data ingestion layer (APIs, webhooks, CSV) with basic hygiene and consent controls 2. Model selection or custom training pipeline using Python, TensorFlow/PyTorch, or another production-ready stack you recommend 3. Lightweight web dashboard (React, Next.js or similar) that surfaces the segmentation cohorts, generated creatives, and forecast reports 4. Clear documentation plus a short Loom walkthrough so my own devs can extend the codebase I value clean, well-commented code, regular check-ins, and evidence of prior work on machine-learning-driven SaaS. If this sounds like your wheelhouse, tell me which frameworks you’d lead with and how long an MVP would realistically take.