A fan in a server and a fan in a treatment plant are the same device at different sizes. So is a booster pump and a midstream mainline pump, a dewatering pump and a process transfer pump. PRISM learns signatures, not one kind of asset. Moving into a new sector is a new application of a proven method, not a new bet.
Strip the industry away and the critical rotating assets are the same population: motors, drives, pumps, fans, compressors, and the loops they sit in. Those features are close to universal across mechanical systems, and they generalize across scale. The models already read them in production, on hundreds of assets, every day.
Remote sites drop off the network for a week and then dump a week of readings in two minutes. Tags carry the name of a contractor from 2009. Half the fleet has no failure history. Most predictive tools require you to fix all of that first. These models were developed against exactly that kind of data: gapped, bursty, mid-stream and mislabeled. They came out of clinical records, which are messier still.
What does not transfer automatically is the tuning: which failure modes matter in your sector, and what it costs you to respond to a flag. A false positive that is trivial at a staffed plant is a wasted day at a remote wellsite. That is set per deployment, with you, and it is the first thing we work out in an evaluation.
Remote, unstaffed, intermittently connected, and catastrophically expensive to lose. The conditions this engine was built to tolerate rather than the ones it needs cleaned up first.
Mainline and booster pumping, transfer and injection pumps, compression, and the drives behind them. An unplanned station outage is throughput lost against a nomination, not just a repair bill. And the sites are hours from the nearest crew, which is exactly when advance notice is worth the most.
Dewatering pumps, slurry and tailings transfer, thickener and process water circuits. Duty is brutal, wear is constant, and a dewatering failure is not an equipment problem. It is a production stoppage and, at the wrong moment, a safety and environmental one.
Continuous flow, tight tolerances, and a downtime cost measured in batch value and compliance exposure rather than repair invoices. That is where advance notice pays for itself twice.
Transfer and circulation pumps, cooling water and chilled water loops, compressed air, vacuum, and the fans and drives across the utilities block. The utilities that nobody owns are usually the ones that take the line down.
Purified water and WFI loops, clean utilities, HVAC serving classified space, chillers and process cooling. Here an excursion is not only downtime. It is an investigation, a deviation, and potentially a batch.
Whatever the sector, the shape is the same. PRISM reads the historian and SCADA telemetry already on site, under a read-only credential, and delivers the decision into the system your crews already use.
One-directional ingest in, decisions out. Nothing in the product can change a setpoint, close a valve or start a machine.
Multivariate signature detection, two-layer sensor-then-synthesis architecture, tolerance for gapped and mislabeled telemetry, and the motor-driven rotating asset population. All of this is running in production today.
Which modes matter in your sector, what your telemetry actually contains, and the sensitivity setting that matches your real cost of intervention. This is the work of an evaluation, and it takes four to eight weeks.
We have a production record in water and live deployments in data centers. We do not have one in your sector yet, and we will not quote you a savings figure until it comes out of your own history. That is what the retrospective is for.
Design-partner terms in a new sector are materially better than list, because the first deployments teach the models something we cannot buy. If you are willing to share history and be a reference, say so on the call. It changes the commercial conversation.
You do not have to believe a claim about portability. You have to look at what the model would have said about failures you already lived through.
What you run, what you already trend, and which past failures you remember well enough to score us against.
Six to twelve months of historian data as flat files under NDA. No connection, no credential, no agent, no security review.
Models tuned to your assets and naming, then run backwards against your real incidents, scored event by event.
At no cost, with no exclusivity and no obligation. If it found nothing, your plant is behaving. Also worth knowing.
Four to eight weeks, entirely offline, at no cost. The only thing at stake is an export and forty-five minutes.