The facil AI-agents squeeze more efficiency out of existing systems (no need for any new sensors or hardware). The plug-and-play facil software gateway integrates directly into the BMS to constantly fine-tune system performance resulting in reduced energy use up to 50% and improved performance by up to 30%. Each AI-agent is a virtual facilities manager that never takes a break---spending every minute making the system work as efficiently as possible.
facil.ai is revolutionizing the building management industry with its advanced AI-powered platform in the cloud, achieving 45% average energy savings for HVAC, refrigeration, and chiller systems. facil.ai also achieves a 40% comfort improvement/reduction in calls and 30% decrease in maintenance costs.
It's real-time Advanced Supervisory Control (ASC) solution autonomously optimizes mechanical systems 24/7, delivering enhanced comfort, reduced energy consumption, reduced maintenance, and fewer service calls. facil.ai integrates with legacy and modern hardware systems, making it easy to implement without any new hardware.
Ideal for commercial buildings, universities, office parks, data centers, healthcare, grocery stores, retailers, hotels, restaurants and more Facil.ai ensures substantial cost savings and operational efficiency.
The industry has been arguing about how to construct smarter buildings for years—Division 25, integrators wanting earlier involvement, consultants pitching their frameworks, prefab vendors pushing integration upstream.
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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