Ento

Buildings account for around 28% of global greenhouse gas emissions, and energy use continues to rise due to slow, costly, and inefficient optimization processes. Identifying savings opportunities, prioritizing actions, collaborating across stakeholders, verifying results, and monitoring ongoing performance are complex and resource-intensive, preventing large-scale decarbonization of the built environment.

Entolab uses scalable machine learning systems to analyze and monitor energy consumption across thousands of public and commercial buildings. Their platform provides actionable insights to optimize energy use, streamline decision-making, and track results in real time. By automating identification, prioritization, verification, and monitoring, Entolab enables faster, cost-effective decarbonization while driving significant energy and CO₂ reductions across the built environment.