RCA 4.0: find the guilty part.
Not the one that just looks broken.
When a machine fails, the worst-looking part is almost never the one at fault: it's the victim of contagion from another. It's the same cause tree you already draw in every RCA report — only here the machine calculates it, with subclinical anticipation, on live data, before the failure finishes happening.
Do you know which components of your asset are invisible to your current instrumentation?
We hand you the map: what it can't detect, which boundaries it can't attribute across, and exactly what sensor fixes it. You don't need to take our diagnosis on faith — it's an audit of your own instrumentation's gaps.
Request the observability diagnosisThe worst-looking part is almost never the guilty one.
A failing component spreads its degradation to its neighbors through heat, vibration, pressure, current, and more. The whole system ends up showing symptoms; only one of them is the cause. This costs money every day: wrong diagnoses, unplanned downtime, and decisions nobody can audit afterward.
of industrial maintenance interventions today are performed on the wrong component, according to sector data.
Wrong diagnosis
The noisiest part gets repaired, not the guilty one. We point to the real cause, even if it looks healthy.
Unplanned downtime
We detect early degradation, many cycles before the equipment actually fails.
Decisions you can't audit
Every result is traceable back to the physical sensor. No models that can't be explained.
From an engineer's heuristics to the topological RCA the machine calculates.
From expert opinion and checklists, to statistical correlation and black-box AI — up to the deterministic algebraic inversion that separates culprit from victim and declares, with a stated reason, when it can't know.
We predict the life of a condition you have never tested.
"I have no failure history for this asset in this condition" is the number one objection to data-driven predictive maintenance. Our method calculates end-of-life a priori, from the component's physical vector, without needing that history — and it abstains, with a stated reason, whenever it falls outside the space it has actually calibrated.
See how we prove itTested by leaving out an entire condition from training (leave-one-condition-out); with a domain gate that abstains outside the calibrated space.
The same mathematics. Sixteen industrial verticals, twenty-two fields of application.
Only the physics of contagion changes: heat, pressure, vibration, current, mechanical coupling, hydraulics, and more. The structure of the method is identical.
Marine & Water
Avoids shutting down a pumping station over a wrong diagnosis.
Energy
Keeps solar inverter performance up as it ages.
Manufacturing & Robotics
Tells apart a genuine fault from an induced one on the production line.
Transportation & Mobility
Anticipates fleet failure before service stops.
HVAC & Climate Control
Avoids replacing the compressor when the filter is the culprit.
The example described in each card is only one of the documented cases in that sector; it does not equal the total number shown.
This isn't theory. It's mathematics validated against documented real failure.
Before an industrial pilot, we test the method against public test benches with real failure and known ground truth. The full detail of all 22 fields of application lives on Solutions.
Pinpoints the exact defect on the raceway
Total accuracy on inner and outer raceway; 99% already at the incipient stage.
End of life does not depend on the sensor
The invariance theorem proves that crossing the end-of-life threshold is the same, no matter what changes about the sensor's brand or scale.
Public reference datasets · real ground truth (NASA-PCoE, Case Western Reserve University).
We declare when we don't know — that's how we guarantee it →
No black boxes. No artificial intelligence.
At its core are deterministic equations with formal mathematical proof — not a model trained on data nobody can audit. Today we deploy the diagnosis half: it measures and calculates the root cause, on live data. The same engine closes the control loop in simulation — bringing it to the plant floor is the next step. The methodology is backed by four patents on file with the Spanish Patent and Trademark Office (OEPM).
See how it worksPatents on file with the OEPM. MEASURE and DIAGNOSE are already deployed in pilot; EVALUATE and ACT are verified in simulation — autonomous control in the field is the next step, with industrial partners.
Do you have a critical asset that can't afford an unexpected failure?
Tell us about your case.
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