Iris
Machine Health & Sensor Intelligence Agent
Most plants know when a machine has failed. Iris tells you before it does - connecting live sensor signals with maintenance history to surface early warnings your team can actually act on.
Where The Problem Lives Today
The data exists, The context doesn't
Plant teams often know when a machine has failed, but they do not always get enough warning before it happens. Heat, vibration, pressure, cycle time, run speed, and other operating parameters may be available from sensors or PLC-connected systems, but the data is usually hard to interpret in the context of actual maintenance history.
A signal without context is just noise
A temperature spike by itself may not mean much. A vibration change may be normal for one asset but concerning for another. The real value comes from connecting live machine behavior with maintenance history, manuals, technician notes, operating conditions, and known failure patterns.
Too many alerts, Too little clarity
Without that context, teams receive too many alerts, miss important early warnings, or spend time chasing signals that do not matter.
Reactive repair is always too late
By the time a failure is confirmed, the damage is done - production is interrupted, parts are emergency ordered, and the team is back in firefighting mode instead of preventing the next one.
What Iris does
Iris monitors live machine behavior and connects it with maintenance history - so your team knows which signals matter, why they matter, and what to do next.
Monitors Machine Operating Parameters
Tracks vibration, heat, pressure, run-time, cycle behavior, and abnormal trends across monitored assets.
Compares Against Asset History and Known Failure Modes
Connects live sensor signals against work orders, asset history, known failure modes, production context, and maintenance team knowledge.
Generates Early Warnings with Context
When a pattern looks abnormal, Iris generates an early warning with a likely explanation, supporting data, suggested checks, and recommended next steps.
Reduces False Alarms
By evaluating signals in context rather than in isolation, Iris filters out noise and surfaces only the warnings that matter.
Learns from Closed Work Orders
Iris can learn from closed work orders to improve future recommendations over time.
Moves Teams from Reactive to Proactive
Iris helps the maintenance team move from reactive repair to informed, proactive intervention.
Works Inside Your Existing Systems
Iris reads from the sensor feeds, PLC-connected systems, and maintenance platforms your team already uses - asset history, work orders, and operating data included.
Your team stays in control
Engineering teams can define the following:
Measured Impact
Better maintenance planning before failure events
Improved reliability insights by asset, line, and production area
What's Next
Once Iris proves herself on machine health and sensor intelligence, the same governed approach extends across other manufacturing workflows. Root cause investigation, repair order review, compliance and inspection documentation, shift handover intelligence, production line performance monitoring - and countless other processes that still depend on people to handle work that is largely repetitive and rule-based, rather than focusing on higher-value decisions.
Your machines are already telling you something. The question is whether anyone is listening.
See how Iris connects live machine behavior with maintenance history - and turns sensor signals into early warnings your team can act on
before failure happens.