Getting Data Out of Machines and Into Decisions
Industrial equipment produces enormous amounts of data that never leaves the shop floor. The value is in capturing it reliably, moving it somewhere useful, and acting on it before something fails.
What we build
Edge & gateway
Collecting from PLCs, sensors and controllers, buffering through connectivity gaps.
Unified namespace
A single hierarchical, event-driven source of plant truth — the architecture decision that should precede protocol and platform choices.
Telemetry pipelines
Reliable ingestion at volume, into time-series storage, with a retention policy decided up front.
SCADA & MES integration
Connecting to existing plant systems rather than replacing what already works.
Condition monitoring
Dashboards and thresholds that tell an operator something actionable.
Predictive maintenance
Models that flag degradation early enough to schedule rather than react.
Digital twins
A live model of the asset or line for simulation and what-if analysis.
Protocols and stack
Two things we will tell you up front
Both come up on nearly every industrial project, and both are cheaper to hear now.
An honest caveat
Sparkplug B is the right default for industrial MQTT, but it mandates QoS 0 — fire and forget. Where every reading is a compliance artifact rather than a trend point, that has to be designed around rather than discovered later. Raw high-frequency retention is also the biggest hidden cost in industrial IoT; downsampling policy belongs in the first design conversation.
Discuss Industrial IoT
Tell us what you're trying to build. We'll help you figure out what comes next.