MODE AI makes sense of messy building data—so you don’t have to. It connects with the systems you already use and does the heavy lifting behind the scenes: organizing, cleaning, and mapping your data. Instead of jumping between dashboards, just ask MODE AI—chat with your data and get instant, clear answers. The result? Actionable insights without the dashboard or spreadsheet chaos. With MODE AI, your data’s not just sorted—it’s ready for what’s next.
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MODE AI is an AI-powered platform that transforms how building owners and operators interact with their data. Founded in 2014 in Silicon Valley, MODE has been at the forefront of integrating Generative AI technology into enterprise IoT solutions, enabling workers worldwide to seamlessly connect with their data and gain valuable insights. By unifying data from various building systems—such as HVAC, lighting, and security—MODE AI provides a centralized platform that simplifies operations and enhances decision-making. Instead of navigating multiple dashboards, users can simply chat with their data to obtain instant, actionable insights. With a growing international team operating out of the San Francisco Bay Area and Tokyo, MODE AI continues to lead the new digital revolution, fostering innovation through open communication, inclusivity, and a commitment to continuous learning.
Amazon's Niharika Kishore described how a model with one technician covering 50 buildings pushed the company toward AI-assisted maintenance for HVAC, refrigeration, and water. The point was not adding more alarms, but finding a way to act on them before breakdowns hit the site.
Hannah Baker, engineer at Willow, walks through how DFW Airport built a CBM program that actually stuck, from training a non-technical QA team to triage thousands of faults, to graduating recurring issues into automated work orders, to tracking a single KPI called 'unsuccessfully actioned' that finally gave leadership visibility into whether closed work orders were actually fixing the problem.
Jose de Castro, CTO of Mapped, shows how one of the world's largest retailers moved restroom operations from schedule-based janitorial rounds to condition-based workflows by combining foot traffic sensors, flush counts, soap levels, and occupancy predictions into AI-summarized work orders that land directly in the existing CMMS, with no new dashboards or tools for technicians to learn.
Brad Dameron from the University of Iowa's Asset Optimization Team and Katie Rossman from Clockworks Analytics walk through how Iowa handles 3,500 faults per day without burying their maintenance shops, showing the exact triage, routing, and closeout workflow they built to turn fault detection into planned work orders that look and feel identical to every other work order in the system.
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