:This paper presents the implementation and experimental evaluation of the Retail Purchase Intelligence System(RPIS), aweb-based platform engineered to automate multi-source price comparison across major e-commerce websites. Building uponthearchitecturalframework established in the first-semester paper, this continuation focuses on the actual construction, deployment, andrigorousperformance testing of the system. The RPIS employs Python-based web scraping technologies —specifically BeautifulSoup, Requests,and Selenium — to extract real-time product pricing data from multiple online retail platforms. The extracteddataundergoesnormalization and storage in a structured MySQL relational database before being presented through a responsivewebinterfacedeveloped using HTML5, CSS3, and JavaScript. Experimental evaluations conducted across five major e-commerce portals demonstrateda price extraction accuracy of 94.7%, an average system response time of 3.2 seconds, and a data normalization success rateof96.1%.The system significantly reduces consumer effort in price comparison while enabling intelligent, data-driven purchase decisions. Resultsvalidate the feasibility and effectiveness of automated retail intelligence in the modern digital commerce landscape. Keywords: E-Commerce, Price Comparison System, Web Scraping, Data Aggregation, Consumer Intelligence, Automation