AI maintenance management
AI maintenance management that operators actually use
We did not set out to build a CMMS. After a year of pilots, every customer told us the same thing about their existing one, and the data inside it was too valuable to leave there. So we built AI maintenance management on top of the same context graph as everything else: a full AI CMMS that is easy enough that operators actually use it, and an agent that reasons across maintenance work the way it reasons across sensor data.
The problem with traditional maintenance management software
Traditional CMMS tools are good at one thing: logging that work was done. They are useful as a system of record. They are not built to reason. They sit in a silo, disconnected from your live sensor data and your process documentation, and they ask the people with the most context (your senior operators and technicians) to spend their time entering data instead of using it.
The result is a system everybody pays for and nobody likes. Data quality suffers because operators avoid the tool. The data that does land in the CMMS sits next to, but never connects to, the OT data your AI would need to actually be useful. Two valuable datasets, kept apart by accident of architecture.
Smart maintenance is not about a prettier form. It is about removing the form when the data is already in the graph, and letting the agent propose the work order the person was about to type.
What AI-powered maintenance looks like in production
Five things that ship today, used by Norwegian manufacturers across food, mattresses, doors, and poultry:
Issues from chat
Type 'new issue on Line 3, motor 3 making a grinding noise' into chat. The agent files the work order, links it to the asset, attaches recent sensor history, and drops it into the queue. No form, no tagging, no tool switching.
Triage with finished investigations
When something deviates, the agent investigates and presents a finished case: what is happening, why it matters, and what the proposed next step is. One click to act, or push back in chat.
Reasoning across history
Ask why the last repair on Pump 4 didn't hold and the agent comes back with the work order, the parts that were swapped, and the conversation around it.
Condition-based scheduling
Templates support time-based, counter-based, sensor-threshold, and manual triggers, combined with gates so you can express 'every 500 hours OR every 6 months, whichever comes first'.
Native app with offline support
QR codes per machine, push notifications, offline support. Operators scan, read, and update from the floor.
Built on the same context graph as everything else
Work orders, maintenance history, spare parts, and asset records sit in the same graph as your sensor data, your documentation, your shift reports, and your team's notes. Two consequences follow.
One, it is easy enough that operators actually use it. No separate login, no separate language, no manual tagging. The agent does the structured part for you.
Two, it is open to the rest of the product. The agents reason across maintenance history the same way they reason across sensor data or process descriptions. See root cause analysis for the investigation flow that uses this, and AI for manufacturing for the broader picture.
Compared to traditional CMMS tools
Side-by-side comparisons against the tools customers usually look at: