How Switch Automation Is Using AI to Cut Building Energy Waste in CO
Switch Automation runs out of Denver, and the problem they chose to solve is one most people never think about: commercial buildings are enormously wasteful, and almost nobody can see where the waste is happening. Their platform pulls data from HVAC systems, meters, sensors, and building management systems that were never designed to talk to each other, then puts all of it in one place. Facility teams at retail chains, corporate campuses, and property portfolios use it to understand what their buildings are actually doing hour by hour instead of guessing from a monthly utility bill. The interesting part is what happens once that data is unified. Switch applies analytics and machine learning to spot fault patterns — a rooftop unit running when the building is empty, a chiller short-cycling, a sensor drifting out of calibration weeks before anyone would notice on a walkthrough. Instead of a technician discovering a problem after the equipment fails, the platform surfaces it as a ranked work order with an estimated cost of inaction. That's the shift: from reactive maintenance to a system that tells you what to fix first and why it matters financially. For their customers, the payoff shows up in two columns. Energy spend drops because equipment stops running when it shouldn't and stops fighting itself when it does. Maintenance costs drop because teams stop chasing symptoms across dozens of sites and start working from a prioritized list. A regional facilities manager who used to spend Monday morning reading complaint emails now opens a dashboard that already knows which three buildings need attention. Multiply that across a portfolio and the competitive advantage is less about any single fix and more about no longer flying blind. What makes Switch a useful model for other Colorado businesses is that they didn't build AI for its own sake. They found a domain where the data already existed, was already being generated every second, and was already being thrown away — t
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