AI-powered predictive maintenance
that pays for itself.
Predict equipment failures 3 to 7 days before they happen, so you can cut unplanned downtime and schedule repairs around production instead of around breakdowns.
What PredictiCare does
Sensors feed the models. The models forecast failures. The maintenance team sees the work coming before the asset goes down.
IoT sensor integration
Connect over Modbus, MQTT, or OPC-UA. Most existing kit works as it is. No rip-and-replace.
Machine learning predictions
Models trained on real failure data. They flag bearing wear, misalignment, and lubrication issues from vibration and temperature signatures.
Real-time monitoring
24/7 telemetry. The dashboard a maintenance manager wants is not the one a finance director needs, so we ship five views over the same data.
Prescriptive actions
Every alert names the part to order and the labour hours required. It also tells you the cost of running the asset to failure if you ignore it.
Built for every operator on the floor
Five role-based views over the same live data. No more spreadsheet handoffs between shifts.
Dr. Hemdan Shalaby
PhD in Mechanical Engineering. Twenty years on rotating machinery, IoT instrumentation, and ML fault detection. Based in Southampton.
PredictiCare packages two decades of asset reliability work into a product mid-sized manufacturers can actually afford. Enterprise vendors quote six figures for the same capability.
Ready to see it on your equipment?
Send us a sensor list and we'll show you how PredictiCare would have caught the last failure you wish you'd seen coming.