E-commerce AI Integration Specialist
Budget: ₹100 – ₹400 INR
I’m ready to bring advanced GenAI capabilities—specifically ChatGPT, Claude, and similar LLM tools—into my e-commerce platform and I need a remote assistant who can handle the technical lift from planning through deployment. The immediate mandate is integration, not model-building from scratch. I want to see three clear upgrades inside the store:
• A real-time personalised product recommendation engine that adapts to browsing behaviour and purchase history.
• A customer-facing chatbot that can resolve common queries, escalate complex issues, and maintain brand voice around the clock.
• A predictive inventory management module that flags low-stock SKUs before they become a problem.
You’ll work with my existing stack (Python, REST/GraphQL APIs, and a React storefront) and any GenAI tooling you find efficient—whether that’s LangChain, semantic search with a vector database, or custom prompt engineering. Code must be clean, documented, and production-ready; endpoints should slot neatly into our current AWS infrastructure.
Acceptance criteria:
• Seamless deployment with no downtime to live traffic.
• Metrics dashboard showing recommendation CTR uplift, chatbot resolution rate, and forecasting accuracy.
• Knowledge-transfer walkthrough so my in-house devs can maintain and iterate on the integrations.
If you have demonstrable success integrating LLMs into e-commerce sites and can start quickly, let’s talk details.
• A real-time personalised product recommendation engine that adapts to browsing behaviour and purchase history.
• A customer-facing chatbot that can resolve common queries, escalate complex issues, and maintain brand voice around the clock.
• A predictive inventory management module that flags low-stock SKUs before they become a problem.
You’ll work with my existing stack (Python, REST/GraphQL APIs, and a React storefront) and any GenAI tooling you find efficient—whether that’s LangChain, semantic search with a vector database, or custom prompt engineering. Code must be clean, documented, and production-ready; endpoints should slot neatly into our current AWS infrastructure.
Acceptance criteria:
• Seamless deployment with no downtime to live traffic.
• Metrics dashboard showing recommendation CTR uplift, chatbot resolution rate, and forecasting accuracy.
• Knowledge-transfer walkthrough so my in-house devs can maintain and iterate on the integrations.
If you have demonstrable success integrating LLMs into e-commerce sites and can start quickly, let’s talk details.