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.

GazeHunt is an innovative tool that generates real-time attention heatmaps, allowing users to visualize where visitors are focusing on their web pages. By employing powerful AI algorithms, it maps out engagement in terms of clicks and views, using color gradients from warm to cool. This data is crucial for optimizing website design, enhancing user experience, and improving conversion rates. Perfect for marketers, UX designers, and website owners, GazeHunt transforms user engagement into actionable insights effortlessly.

Rep.ai provides AI-powered live video chat solutions designed to convert website visitors into leads and customers. It features Human + AI live chat that allows engaging visitors through text, video, or audio, enables scheduling meetings, detecting and tracking accounts, and building dynamic visitor lists. The AI chat can operate 24/7 ensuring no visitor is missed, capturing buying intent signals from various sources and integrating seamlessly into existing workflows.