AI-Powered Cross-Platform E-Commerce Search and Ranking Platform Using Agentic AI
G Lakshmi Swathi,
Shaik Mohammad Ravuf,
Shaik Abdul Farook,
Pinniboyani Surya,
Singaboina Pavan Krishna
The proposed AI-powered cross-platform e-commerce search and ranking platform uses Agentic AI
to coordinate multiple autonomous agents for user-intent interpretation, real-time data aggregation,
product comparison, sentiment analysis, dynamic ranking, and personalized recommendation. The
system is designed to support seamless interaction among AI agents, heterogeneous e-commerce data
sources, and end-user interfaces through a modular, scalable, and performance-aware architecture.
The platform emphasizes real-time responsiveness, adaptive ranking models, continuous learning from
user behavior, and efficient inter-agent coordination to improve relevance, transparency, and decision
quality in online shopping. The proposed design also focuses on reducing user effort during product
discovery by unifying search over multiple marketplaces and presenting a clear ranked output supported
by source-aware reasoning. This makes the system suitable for intelligent commerce environments where
product information changes frequently and user expectations are highly personalized.
