Maintenance and reliability teams can ask about machine health in their own words and get an expert-level analysis and maintenance recommendation, with the reasoning shown. The AI colleague is already in daily use at Metsä Group, SSAB and Aurubis.

“Traditional condition monitoring software raises an alert and leaves the interpretation to an engineer. Rotomate’s AI colleague analyses the root cause and recommends what to do,” said Mikko Kuusisto, CEO and co-founder of Helsinki-based Rotomate.

“Now you can also ask it follow-up questions in plain language, the way you would talk a problem through with an experienced colleague. This is a big shift for the industry: how well you know the condition of your machines no longer depends on how many specialists you happen to have on site.”

Industrial plants have accumulated years of condition monitoring data, but there are rarely enough specialists to interpret it all. Vibration analysts are scarce, their expertise takes years to build, and a single analyst is often responsible for hundreds of machines. When a critical signal is missed, the cost is high: the failure of one critical machine can cause close to €1 million in lost output within a single shift.

Rotomate’s AI colleague works through machine data the way an experienced reliability engineer does: trends, fault frequencies, components and machine history. It understands the plant’s context and shows the evidence behind every conclusion.

“The conversation about generative AI in reliability is still mostly theory and chatbot pilots. For us it is in daily use at customer sites. Our AI colleague is not a chat layer on top of alerts. Instead, it reasons directly from the machine’s measurement data, process variables and maintenance history, and shows what its answer is based on,” said Jesse Miettinen, CTO and co-founder of Rotomate.

From now on, expert-level condition monitoring is no longer reserved for large plants with in-house specialists: smaller sites get the same up-to-date picture of their machines. Anyone on the maintenance team can ask for a recommendation directly, where previously every question went through the analyst. The specialist’s job does not disappear, it changes. Based on customer deployments, time spent on manual condition monitoring tasks falls by up to 87%, and one analyst can cover many times more machines. The AI recommends, people decide.

“We use Rotomate’s AI colleague in our maintenance work every day. Recently it flagged a developing bearing fault on a slowly rotating machine that threshold-based monitoring had not picked up. When something comes up, I ask it directly what the finding is based on and how urgent it is, which helps me decide what to do,” explained Petri Sillanpää, condition monitoring specialist at Metsä Group.

Plants can try the AI colleague free of charge on their own route measurement data. Rotomate presented the new version publicly for the first time in a webinar on 8 October, walking through how the AI colleague finds a developing gear fault on a Metsä Group machine and how to ask it follow-up questions.

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