Butlr’s wireless occupancy sensor offering combines anonymous, heat‑based sensing with edge AI to deliver reliable occupancy detection, space‑utilization analytics, and energy control without capturing cameras or personally identifiable information. The platform is designed for facility managers, lighting contractors, and energy teams who need accurate detection in low‑motion spaces, simplified retrofit installations, and enterprise integration with building systems.
A wireless occupancy sensor detects the presence or absence of people and reports that state to controls (lighting, HVAC, BMS) or analytics platforms without a wired connection to each control point. Unlike simple motion sensors that report immediate movement, modern wireless solutions may combine sensor fusion and AI to deliver more reliable presence detection, dwell times, and aggregated space‑utilization metrics.
Butlr uses heat‑based (thermal) sensors that detect the relative distribution of body heat in a space rather than capturing visual images. Each sensor runs on a small local processor that applies machine learning models to convert thermal patterns into anonymized occupancy events and aggregated metrics.
Thermal sensing with AI addresses several shortcomings of conventional PIR motion sensors and provides additional capabilities for building operations.
Wireless thermal occupancy sensors serve a wide range of commercial and institutional spaces where privacy and accuracy matter.
Butlr’s wireless occupancy sensors are designed for flexible integration and practical deployment in retrofit and new builds.
While exact specifications vary by model and site requirements, typical performance characteristics for enterprise thermal occupancy sensors include:
Always verify model‑specific datasheets and perform on‑site validation to confirm performance in your environment.
Wireless occupancy sensors can deliver measurable energy and operational savings, especially when paired with intelligent controls and analytics.
Office retrofit with wireless thermal sensors and integrated lighting control: 40% lighting energy savings, 15% HVAC runtime reduction, and estimated payback within 18–36 months depending on local rates and incentives.
Thermal sensors do not capture images or video. Data is processed into anonymized occupancy events and aggregated metrics. Privacy controls and data retention policies ensure no personally identifiable information is stored.
Yes. Wireless sensors can feed occupancy events to many controllers and building automation systems. Integration can be direct via relay/event outputs or through gateways and cloud APIs for broader analytics.
Performance scales with sensor density. For large open areas, a networked deployment with overlapping coverage provides reliable occupancy and flow estimates. A site survey will identify optimal sensor density.
Maintenance is typically low: periodic battery replacement (if battery‑powered) or standard checks for wired units. Firmware updates and model improvements are delivered over the network in managed deployments.
Deployments commonly integrate with lighting controls, BMS/BAS platforms, and cloud analytics via standard building protocols and APIs. Discuss your target controls platform during procurement to confirm compatibility.
If you are evaluating wireless occupancy sensors for energy savings, occupant comfort, or space analytics, consider a phased approach:
Common next steps: request a demo, run a site assessment, or start a pilot program to measure expected savings and operational benefits.
If privacy, retrofit speed, and analytics beyond simple motion detection are priorities, thermal AI‑driven wireless occupancy sensing provides a practical path to smarter, more efficient buildings.
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