CityLearn provides a modular simulation platform for energy management research using reinforcement learning. Users can define multi-zone building clusters, configure HVAC systems, storage units, and renewable sources, then train RL agents against demand response events. The environment exposes state observations like temperatures, load profiles, and energy prices, while actions control setpoints and storage dispatch. A flexible reward API allows custom metrics—such as cost savings or emission reductions—and logging utilities support performance analysis. CityLearn is ideal for benchmarking, curriculum learning, and developing novel control strategies in a reproducible research framework.

TAHO is designed to optimize AI, Cloud, and High-Performance Computing (HPC) workloads by removing inefficiencies and enhancing performance without the need for additional hardware. It provides instant deployment, automated scaling, and real-time monitoring to maximize resource utilization. By autonomously distributing workloads across various environments, TAHO ensures operational readiness and peak efficiency, reducing operating costs and power consumption. With TAHO, businesses can achieve faster execution, reduced training costs, and enhanced throughput for compute-intensive tasks, making it a valuable solution for any infrastructure.

Q-Bot develops intelligent robotic systems designed to inspect, monitor, and insulate building underfloors. These systems leverage AI and state-of-the-art robotics technology to apply high-quality foam insulation efficiently and with minimal disruption. This innovative approach significantly reduces heat loss in buildings, leading to substantial energy savings and improved living conditions. By enhancing thermal performance, Q-Bot helps homeowners and landlords achieve higher energy efficiency standards.