The Mall Recommendation Multi-Agent System is an AI-driven framework built on a multi-agent architecture to enhance retail experiences in shopping malls. It consists of shopper agents that track visitor interactions, preference agents that analyze past and real-time data, and recommendation agents that generate tailored product and promotion suggestions. Agents communicate via a message-passing protocol to update user models, coordinate cross-agent insights, and adjust recommendations dynamically. The system supports integration with CMS and POS for real-time inventory and sales feedback. Its modular design allows developers to customize agent behaviors, integrate new data sources, and deploy on various platforms. Ideal for large retail environments, it improves customer satisfaction and boosts sales through precise, context-aware recommendations.

Mediar is an AI-powered retail video analytics platform designed to give retailers and brands deep insights into their conversion funnel, starting from traffic, engagement, and through to final conversion. By leveraging advanced AI and video technologies, Mediar helps businesses understand customer behavior, optimize store layouts, and enhance overall sales performance. It turns video data into actionable insights, allowing retailers to make informed decisions to boost customer engagement and improve conversion rates.