The data predictive maintenance needs is usually already sitting in your PLCs and maintenance logs, disconnected. We build the pipeline that turns it into a warning, before you buy new sensors or a new platform.
Most predictive maintenance pitches start with new sensors and a new platform. Ours starts with what’s already running: run hours, cycle counts, temperature, vibration, fault codes, most of it already being captured somewhere and going nowhere. The gap usually isn’t missing data. It’s that nobody built the pipeline to pull it into one place and watch the trend.
Unplanned downtime that had warning signs. Vibration was climbing, cycle times were creeping, a fault code kept throwing intermittently. The evidence existed for weeks. Nobody was watching for it.
Equipment data scattered across systems. PLC data, maintenance tickets, run logs, and inspection sheets each hold a piece of the picture. None of them get compared against each other over time.
Predictive maintenance projects that stall on the model. Teams jump straight to machine learning before there’s a clean, connected data pipeline for the model to learn from, and the project never ships anything usable.
Maintenance decisions made after the failure, not before it. By the time a breakdown gets logged, the opportunity to catch it early is already gone.
We start with the single piece of equipment causing the most unplanned downtime, or the one where a failure is most expensive. We connect the signals that asset already produces, set threshold-based alerts on the ones that matter, and get a maintenance lead notified before the failure instead of a supervisor notified after it.
That’s not a machine-learning project. It’s a connected pipeline and a dashboard, and it catches most preventable downtime on its own. Once it’s proven on one asset, we expand to the next one, and only build toward predictive modeling once there’s clean data worth training it on.
A 30-minute call is enough to know whether the data to predict its next failure already exists. No pitch decks, no commitment.
Start a Conversation →