TrendScanner AI: An Agentic Intelligence-Based Framework for E-Commerce Trend Analysis and Product Recommendation

– The exponential growth of e-commerce has led to vast amounts of consumer-generated data, including reviews, ratings, pricing variations, and shifting shopping behaviors. Traditional analytics pipelines suffer from limited adaptability, manual intervention, and the inability to provide actionable insights in real time. This paper introduces TrendScanner AI, a modular agentic system designed to automate trend analysis, price forecasting, and AI-driven product recommendations using the Gemini 1.5 Flash large language model. The system integrates a multi-agent reasoning layer, a Streamlit-based visualization dashboard, Firebase Cloud infrastructure, and Docker + n8n workflow automation. Real-life datasets demonstrate improved insight generation, faster processing pipelines, and enhanced interpretability. Results show a 40–55% reduction in manual analytical labor and >93% consistency with marketplace trends. TrendScanner AI illustrates how AI-driven automation can transform modern commerce analytics and product development strategies.