Your assets are already telling you they are about to fail. The signal is spread across temperature, load, vibration and a dozen other channels, and nobody reads it. Ascendo learns what normal looks like for each individual unit, finds the correlated signals that actually drive failures, and scores the risk before the failure surfaces.
Reliability teams react after the issue, with limited visibility into the patterns building underneath, which leads to expensive escalations and decisions made on instinct.
Failure signals are correlated across channels rather than visible in any single one. A temperature rise alone is noise; the same rise with a particular load pattern and a vibration signature is a bearing about to go. So the alarms that fire are frequently the wrong ones, and the team learns to discount them.
The output is not an alarm. It is how likely something is, and how sure the system is about it.
Multi-dimensional time series telemetry builds an intelligent asset fingerprint, a continuously learned picture of what normal and abnormal look like for that asset specifically.
A deep learning model looks for the correlated signals that actually drive anomalies, rather than for an isolated spike on any one channel.
Analysis runs per serial number, predicting issues before they surface, with a confidence attached rather than a bare verdict.
Engineers annotate what the system got right and wrong, and those corrections persist, so the model tunes to your fleet.
The competitor here is not another vendor. It is the alert fatigue your team already lives with.
A single channel crossing a line says very little. The pattern across channels is what precedes the failure, and it is invisible to any alarm watching one number at a time.
“The system continuously learns normal and abnormal behavior, identifying correlated signals that actually drive anomalies, not just isolated spikes.”
Any system that adds to the alert pile has failed, however accurate it is. Filtering across alert signals is part of the job here rather than a setting somebody tunes afterwards.
“Our agents can also go through the noise across alert signals and filter what is important.”
It will not understand your equipment at first. Annotations let a reliability engineer say what a signature really meant, and that correction persists.
“identifying true correlations, providing risk and confidence scoring, and enabling a human feedback loop through annotations, so the system continuously improves”
Most reliability teams are running at least two of these today.
Fire on isolated spikes, blind to correlated patterns, and they produce the alert fatigue that has to be beaten.
Shows the history accurately and waits for a human to notice a pattern in it. Noticing is the step that does not happen.
A good team can build the model. The gap is the operational loop around it: scoring per unit, filtering noise, capturing annotations.
Services healthy assets on a calendar and still misses the one that fails early.
The vocabulary used in the recording.
The adjacent agent on the same diagnostics mesh, covering device, system and field service logs.
Where a detected anomaly becomes a probable cause rather than another thing to investigate.
What a predicted failure is worth commercially: the part reserved before the technician needs it.
Asset Intelligence
Lead-time claims are easy to make and hard to trust. Give us telemetry from assets that have already failed and we will show you when the risk score would have moved.
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