AI E-Commerce Sales Boost Platform

My goal is to lift online revenue by building an e-commerce platform that serves each shopper a real-time, AI-driven stream of product suggestions. The entire architecture—from data ingestion to user-facing widgets—should be engineered around this single purpose: increasing sales through highly relevant recommendations. Core functionality The heart of the build is a personalised recommendation engine. It should analyse browsing behaviour, purchase history, and contextual signals to deliver on-page and in-email product suggestions that feel hand-picked for every visitor. While dynamic pricing and automated marketing campaigns may follow later, phase one focuses exclusively on perfecting these recommendations. Technical expectations • End-to-end platform or plug-in capable of integrating with common stacks (Shopify, WooCommerce, custom React/Node, etc.). • Scalable data pipeline—batch and real-time—to capture events, train models, and serve predictions with low latency. • Model layer leveraging proven libraries (TensorFlow, PyTorch, or similar) and techniques such as collaborative filtering and deep learning for cold-start mitigation. • Admin dashboard for A/B testing, rule overrides, and performance analytics (CTR, AOV lift, revenue attribution). Deliverables 1. Deployed, production-ready storefront or extension with live product-suggestion widgets. 2. Source code repository with clean commit history and automated tests. 3. Infrastructure-as-code scripts (Docker/Kubernetes or equivalent) for reproducible deployment. 4. Documentation: setup guide, API spec, data schema, and a short walkthrough video. Acceptance criteria • Recommendation latency under 200 ms at P95. • At least a 5 % uplift in click-through rate during pilot A/B test against a static control list. • No critical errors in load testing at 5× expected peak traffic. Timeline is flexible within reason, provided milestones are met and performance targets are verifiable. I’m available throughout for dataset provisioning, brand assets, and iterative feedback.