Butlr operates a thermal-based occupancy platform that ingests sensor data at scale without capturing identity. The tech stack—Go, MQTT, NATS, InfluxDB, Prometheus, and edge compute layers—reflects the core challenge: streaming sensor telemetry from distributed devices into real-time analytics. Active projects center on low-latency edge processing and distributed event pipelines, while documented pain points (scaling real-time ingest, edge compute latency) map directly to the infrastructure they're building.
Butlr builds an occupancy and spatial intelligence platform for enterprise workplace planning and operations optimization. The system detects human presence and movement patterns using thermal sensors and AI—specifically body-heat analysis—without recording identity or personal characteristics. The company serves mid-market and enterprise customers managing physical spaces, from office utilization to facility optimization. The platform operates as a data collection and analytics layer deployed across customer sites globally, requiring both on-site hardware coordination and cloud-side real-time processing.
Go, MQTT, NATS, InfluxDB, Prometheus, Grafana, GraphQL, Terraform, AWS, Docker, Python, OpenCV, and PyTorch. The mix emphasizes real-time streaming (MQTT, NATS), time-series storage (InfluxDB), and ML inference (OpenCV, PyTorch).
Yes. Butlr has open engineering roles, currently focused on senior and lead positions. Five total roles are active with minimal posting velocity; hiring spans the US and Japan.
Low-latency edge compute platforms, distributed event pipelines, real-time sensor data processing infrastructure, and global deployment capabilities. Recent focus areas include on-site sensor deployments and smart building APIs.
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