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Predictive maintenance software

Optimize machine availability & maintenance cycles

You are looking for a tool to prevent machine downtime? Our Predictive Maintenance system detects early signs of abnormal machine behavior – enabling effective predictive machine maintenance.
Automated fault detection & notification
Plug-and-play system with KSB sensors available
No complex IT projects, fully scalable at any times
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Increased uptime
by up to 20 %
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Up to 17% lower maintenance costs
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Easy integration for all types of machines
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Scalable for the entire shop floor

aiomatic is already monitoring > 1,000 machines

Numerous customers benefit from AI-supported monitoring

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Identify machine faults early with aiomatic. Avoid downtime.

An unplanned machine downtime can bring production lines to a standstill – resulting in delivery delays, stress, and high costs. Our software serves as a powerful Predictive Maintenance tool to detect impending faults at an early stage and alerts you in time – clearly visualized in an intuitive dashboard. This way, you secure your delivery schedules, reduce downtime, and lower repair costs.
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Optional: Our solution with
Plug & Play sensors from KSB

Together with our partner KSB, we offer you an all-round, worry-free package that covers the entire process: from sensor installation to continuous fault monitoring.
Ideal for rotating machinery from all industries

Quick plug & play: Attach sensors & receive data

Highly scalable system, regardless of machine manufacturer

Request your quote for Predictive Maintenance with AI

Fill out the form with the most important information about your machines. Your data will be securely transmitted to us and our team will prepare an individual offer tailored to your requirements.
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Success stories: Predictive machine maintenance made easy

Large machine plant in the energy sector
Gas storage & compressor
Monitoring of the entire system, in particular the fast-rotating, sensitive turbines.
Illustration of a complex coating system
Inline coating machine
Monitoring of the pumps required for the water cycle in the coating plant.
Section of a machine for processing animal feed
Milling machines for animal feed
Monitoring of storage temperatures and drive performance for reliable & efficient production.
Conveyor belts in port operations
Conveyor belts in port operation
Continuous monitoring of motors, gearboxes and bearing blocks to prevent bearing damage and belt ruptures at an early stage.
Manufacture of bicycles
Gearmotors in bicycle production
Condition monitoring of drives and gears to avoid downtimes in conveyor technology and interruptions in
production.
Crunchy plant in food production
Crunchy plant in food production
Monitoring of agitators, mixers and conveyors to ensure product quality and continuous production.
Discover success stories
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"Thanks to the early warning from aiomatic, we were able to detect a gearbox failure in time. As a result, we were able to maintain operations over the weekend with reduced output and prepare for the necessary repair, instead of being surprised by a sudden imminent failure."
Ben Thurnwald
Managing Director
Logo Canyon
"The future of maintenance is difficult when it comes to skilled labor. With the AI-based solution from aiomatic, however, this looks much brighter. The team can now focus on other activities, while monitoring and early warning alerts are provided by aiomatic."
Andreas Weber
Technical maintenance manager
“aiomatic is pioneering. For example, the application of AI to real-time data has already predicted imminent warehouse damage for one of our systems. As a result, we were able to act early and avoid an unplanned downtime of more than 8 hours. ”
Dirk Schlamann
Lead Maintenance
"The set-up with aiomatic was surprisingly straightforward. The seamless integration into existing workflows and the intuitive operation of the dashboard make the solution a real success that quickly delivers added value for our team."
Ben Thurnwald
Managing Director
“The aiomatic solution offers a fast and reliable way to achieve a zero-defect goal in the area of predictive maintenance compared to traditional methods.”
Christian Bittel
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