It is a working AI service tied to your machines and your ERP, not a dashboard you have to watch. Here is what it reads, what it predicts, and how a warning turns into a scheduled repair.
The service pulls in vibration, temperature, load and run hours from your machines and sensors, and joins them with maintenance history and ERP records like work orders and downtime. It learns each machine's normal range instead of using a generic threshold.
Instead of a single alarm, the model gives a health reading per machine and an estimate of how long you have before it is likely to fail. That lead time is what lets you plan the repair rather than scramble when the line stops.
Each prediction turns into a maintenance task your team can slot into a shift that suits the plan. The warning lands where your maintenance team already works, so it drives action instead of sitting in yet another tool nobody checks.
Every tracked machine watched in the background against its own normal range, around the clock.
A health reading and an estimated time to failure for each machine, so you know where to look first.
Warnings raised as maintenance tasks with the machine, the likely issue and a suggested window.
A simple view of machine health, open warnings and downtime, tied back to your ERP data.
The AI approach that fits your machines, trained on your own history, with no vendor lock-in.
Every confirmed failure and repair feeds back in, so the predictions get sharper as it runs.
Book a free consultation. We will walk through your machines, the data you already collect and your ERP, and scope predictive maintenance for your floor.
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