AI E-Commerce Sales Boost Platform
Budget / Salary₹12,500–37,500
TypeFreelance project
LocationRemote
Posted1 hour ago
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.
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.
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