Predictive maintenance informed by equipment data

We monitor micro-vibrations in rotating machinery to detect bearing wear months in advance.

Machines chatter before they crash. We put ears on them to hear the subtle shakes and rattles that mean a bearing is about to give up.

Heat signatures reveal electrical faults and friction points that are invisible to the naked eye.

Hot spots are bad news. We use thermal cameras to find the parts that are running feverishly high so you can swap them out before they melt.

We listen for ultra-high frequency sounds that indicate cracks forming in materials.

It's like listening for a pin drop in a tornado. We can hear the tiny "ping" of metal cracking long before you can see any damage with your eyes.

Our ML models learn "normal" behavior and flag any deviation instantly.

Ideally, the system learns what a "good day" looks like. The moment something weird happens-like a fan spinning 1% slower-it flags it instantly.

We integrate with your calendar to schedule repairs during planned downtime, not emergencies.

Fix it on your terms. We tell you, "This motor has 200 hours left," so you can replace it next Tuesday afternoon, not at 3 AM when the line explodes.

We track exactly how much money you save by preventing downtime and extending asset life.

At the end of the year, we show you the receipts. "You spent X on us, and we stopped Y crashes, saving you Z million dollars." Simple math.

We integrate fluid sensing to detect contamination or degradation in hydraulic and lubrication systems.

Oil is the blood of your machine. We check for metal shavings or water content that suggests internal damage, catching issues that vibration sensors might miss.

Monitor the health of assets across different factories or locations from a single command center.

Whether you have one factory or ten, see them all in one place. We aggregate health scores so you can allocate maintenance resources to the site that needs them most.