PRISM reads the telemetry you already collect and tells you how long each asset has left, with the probable fault named.
This is the view a maintenance planner opens. Not a health score to interpret, but every monitored asset laid out against the clock.
Each bar is one asset's predicted days-to-failure window. Where it sits is when to act. How dark it is is how certain and how severe. Sorted soonest first, so a planner works top to bottom.
A threshold is a rule an engineer wrote for a failure they had already seen. The ones that take you down are the ones with no rule attached.
A threshold trips once the failure is underway. The drift that led there was readable for months in data you were already collecting.
Sites already carry hundreds of standing alarms. The question is never more alerts. It is which of them deserves a truck.
You cannot write a rule for a failure mode you have not met yet. A model that learns the whole signature can still see it coming.
Time-series telemetry in, time to failure out. The engine learns each asset's whole operating signature rather than a list of rules, so it flags drift no one had written a warning for.
One-directional ingest in, decisions out. Nothing in the product can change a setpoint, close a valve or start a machine.
391 assets across eleven states, nine months, zero field hardware. Treatment works, wells, booster and high-lift pumps, lift stations.
Chillers, towers, CRAHs, CDUs and coolant loops; UPS, switchgear and distribution. Same asset class, denser instrumentation, different stakes.
Midstream pumping, mining dewatering, chemical and pharmaceutical plant. Remote sites and gapped telemetry are the normal case, not a blocker.
No write path to your controls exists in the product, and nothing installs on your network. PRISM analyzes; your people act.
Every asset sorted by how long it has left, so attention goes to the few that need it.
Days to failure, confidence and the top driver behind each flag, with the action attached.
Per-sensor evidence behind every score, so a planner can check the reasoning before spending a truck roll.
Captured from a production water deployment. Site and utility names anonymized.
Nine months, 391 assets, measured against one operator's own cost records. Almost none of it came from avoided outages; it came from asset-level failures caught before they escalated.
Return on spend, against the operator's own records
Consecutive failures flagged before they happened
Realized savings per monitored asset, per year
On a separate eight-week lift-station validation
A scoping call, a model on your assets in four to eight weeks, and a retrospective scored against failures you already remember.
No cost to evaluate. No live connection required. No obligation.