Butlr

Anonymous spatial analytics sensing only heat signals, knowing how many people but not who

Contact for pricing United States

Butlr does spatial intelligence, using low-resolution thermal sensors to judge how many people are in a space, where they are, and how long they stay. Their slogan puts it plainly: 'We only sense heat. It's that simple.' — a sentence that's both a technical explanation and a privacy commitment.

Features and application scenarios

Why use thermal sensing instead of cameras? Because most spatial-analytics needs don't require knowing 'who this person is,' only 'whether there are people here and how many.' Cameras can give you the answer but bring a whole set of privacy, compliance, and employee-pushback headaches. Butlr's Heatic 2 and Heatic 2+ sensors read only heat signals, at a resolution too low to identify individuals, yet enough to do headcount and movement-path analysis. The platform uses an API-first design, so data can feed into an existing BI or facility-management system. They recently launched Butlr Care GPT, letting caregivers directly ask in natural language questions like 'which room's path design easily leads to falls.' It currently serves over 200 enterprises across 22 countries, with applications spanning office spaces, long-term care, university campuses, and smart buildings; bases are in Burlingame, California, Cambridge, Massachusetts, and Tokyo.

Best suited to scenarios needing space-utilization analysis but not wanting (or unable) to install cameras — office planning, long-term-care institutions, education spaces, retail path flow. Taiwanese enterprises have been pushing hybrid work and space downsizing in recent years, and what's most lacking is an objective number like 'how much are our meeting rooms actually used'; doing this with cameras would spark pushback in Taiwan's office culture, and thermal sensing is a relatively smooth path.

Main features

Heatic 2/Heatic 2+ thermal sensors, reading only heat signals, unable to identify individuals
Real-time headcount, occupancy, and dwell-time analysis
Movement-path and space-flow analysis
API-first data platform, connectable to existing BI and facility-management systems
Butlr Care GPT: query spatial and care insights in natural language
Battery-powered, wireless deployment, no wiring needed for installation
Applicable to office, long-term care, education, smart buildings, and more

Common uses

Actual-utilization analysis of office meeting rooms and workstations
Space-downsizing and reconfiguration decisions under hybrid work
Room-path and fall-hotspot analysis in long-term-care institutions
Space-use scheduling for university classrooms and libraries
People-flow and dwell analysis in retail stores
Adjusting HVAC and lighting by actual occupancy to save energy

Key Features

Heatic 2/Heatic 2+ thermal sensors, reading only heat signals, unable to identify individuals
Real-time headcount, occupancy, and dwell-time analysis
Movement-path and space-flow analysis
API-first data platform, connectable to existing BI and facility-management systems
Butlr Care GPT: query spatial and care insights in natural language
Battery-powered, wireless deployment, no wiring needed for installation
Applicable to office, long-term care, education, smart buildings, and more

Pros

The privacy design is decided at the hardware layer, not by software promise — a completely different persuasiveness
Wireless battery-powered deployment, no rewiring needed, low adoption friction
API-first lets data truly flow into the enterprise's existing analytics process
Care GPT lets non-technical care or facility staff pull data themselves

Cons

AI isn't the product's core; the main value is the thermal-sensing hardware and data platform, with less analytical depth than image solutions
Thermal sensing inherently obtains limited information and can't do behavior-detail interpretation
Sensor count increases linearly with space area, so hardware cost is considerable for large sites
Pricing not public; battery-powered devices have the long-term operational burden of replacement and maintenance

Use Cases

Actual-utilization analysis of office meeting rooms and workstations
Space-downsizing and reconfiguration decisions under hybrid work
Room-path and fall-hotspot analysis in long-term-care institutions
Space-use scheduling for university classrooms and libraries
People-flow and dwell analysis in retail stores
Adjusting HVAC and lighting by actual occupancy to save energy

Editor's Note

Editor's note: I quite appreciate Butlr's willingness to make 'we only sense heat' a selling point on the official site rather than piling on AI buzzwords. This is actually a very disciplined product decision — they proactively gave up the rich information cameras can provide in exchange for a clean privacy stance. For pushing office space analysis in Taiwan, I think this trade-off is right: if you use cameras to count workstation utilization, the internal inbox will explode before the numbers even come out.

FAQ

Can thermal sensors really not recognize who it is?

Butlr's sensors are deliberately designed at very low resolution, reading heat blobs rather than face or body-shape details, and by design can't do individual identification. This is a fundamentally different privacy architecture from 'has footage but we promise not to look.'

Is this an AI tool?

To be honest: AI isn't Butlr's core technology — the thermal-sensing hardware and data platform are. They have an AI analytics layer (including Care GPT), but if you're looking for deep behavior analysis, image-based solutions can give more. Butlr's positioning is 'enough information at the lowest privacy cost.'

How is it different from just using an entrance counter?

An entrance counter only knows the total in and out, while Butlr can tell you the distribution inside the space — which desk is often used, which corner no one goes to, where people dwell longest. When doing space planning, this granularity difference is crucial.