QNX and Sift are bringing sub-second telemetry queries to industrial edge hardware, streaming live data from embedded factory machinery.
The integration targets mission-focused machinery operating on factory floors, robotics cells, and medical environments. By connecting Sift’s data infrastructure platform to QNX OS, engineering teams can inspect machine states within one second of telemetry leaving an edge device. Operating teams gain immediate visibility into physical performance without altering the binaries or software builds executing on their hardware.
Austin Spiegel, Co-Founder and CEO of Sift, said: “The next decade of hardware will be won by the teams that learn fastest from their machines.
“QNX runs the machines the world cannot afford to get wrong. A team should be able to plug in on day one and spend their time engineering, not building telemetry plumbing, and that is exactly what this partnership delivers.”
Sub-second SQL queries over MQTT broker feeds
The technical workflow targets edge installations powered by QNX OS 8.0. Embedded sensors dispatch thousands of simultaneous readouts across separate control subsystems. Instead of writing custom logging routines or deploying external collector agents, developers feed existing internal messaging paths directly into Sift.
Sift ingests data by subscribing to native telemetry broadcasts already active within QNX OS, such as MQTT feeds, while accepting proprietary transmission formats through custom ingest paths.
In practice, an automation controller or industrial robotic arm streams device telemetry through its local MQTT broker. The platform ingests those messages and makes them immediately searchable using standard SQL within one second.
How QNX and Sift are correlating industrial subsystem health across time
Physical edge systems generate asynchronous operational records that often defy standard diagnostics. A single automated station might track motor torque, thermal excursions, bus latency, and system memory across separate components. Sift arranges these disparate data streams along a unified chronological index.
Plant engineers can track an intermittent operational fault directly against the processor load that preceded it. They can also benchmark a newly commissioned machine tool against operational traces recorded from the previous hundred units deployed across assembly lines.
QNX software, developed by BlackBerry Limited, runs in more than 275 million vehicles globally and forms the control core for industrial automation systems, heavy equipment, and robotics assemblies built by suppliers such as Bosch and Continental. As factories deploy physical AI systems that make autonomous runtime decisions, access to raw sensor telemetry determines how quickly plant teams detect hardware anomalies.
Romain Saha, Senior Director of Strategic Alliances at QNX, commented: “Our customers build systems where safety and real-time performance are non-negotiable, and they are increasingly asked to do more with the data those systems generate.
“Sift gives them a validated, low-friction path from a QNX-powered device to real-time analysis and as physical AI reshapes industries, access to real-world operational data is becoming a critical enabler of innovation.”
Learn more about physical AI during the Physical AI Expo held in Amsterdam, London, and North America.
See also: NVIDIA Jetson Thor speeds edge agentic inference in MLPerf v6.1
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