Butlr’s People Sensing Platform provides occupancy and indoor traffic data, enabling companies around the world to make data-driven decisions around space management and operations. Employers use Butlr technology to create supportive and collaborative work environments. Commercial real estate professionals use Butlr technology for private and accurate insights on office usage to offer flexible leasing options while executing a smart building strategy featuring more energy efficient properties with a lower carbon footprint. For senior living facilities, Butlr’s anonymous, ambient sensing technology can enable passive check-in for caregiving by understanding movement and flagging unusual activity.
Through its thermal sensors, Butlr translates heat into human presence. Low-resolution thermal sensing makes the Butlr hardware 100% identity-agnostic and private. The sensors are wireless and can be magnetically installed, simplifying an otherwise complicated installation process, and significantly cutting down its cost.
The data captured by the sensors can be consumed either directly through Butlr’s API, or through an analytics platform, provided by one of the Butlr Partners around the world. Butlr also offers a basic dashboard for customers to get a glimpse of the power of spatial insights as they are getting started.
Stanford University built a weather-driven curtailment risk dashboard and a live electrical capacity monitor on top of its utility data historian, giving campus operators early warning before cooling or electrical capacity limits are reached.
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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