The Multi-Agent Autonomous Waste Collection System is a research-driven platform that employs multi-agent reinforcement learning to train individual waste-collecting robots to collaborate on route planning. Agents learn to avoid redundant coverage, minimize travel distance, and respond to dynamic waste generation patterns. Built in Python, the system integrates a simulation environment for testing and refining policies before real-world deployment. Users can configure map layouts, waste drop-off points, agent sensors, and reward structures to tailor behavior to specific urban areas or operational constraints.

Quensus specializes in intelligent water management with its flagship product, LeakNet. Using the latest in cloud computing and machine learning, LeakNet delivers real-time water monitoring, leak notifications, and automatic shutoff to create leak-free environments. This comprehensive solution not only helps in preventing water damage but also significantly reduces water bills by up to 60%. It's designed for both commercial and residential properties, ensuring safety, sustainability, and cost-efficiency.

Scrapeless leverages artificial intelligence to collect, process, and analyze scrap data efficiently. It provides businesses with vital insights into material quantities and values, streamlining recycling operations while minimizing errors and improving overall profitability. Additionally, Scrapeless ensures sustainability by promoting responsible waste management practices through data-driven decision-making.