Evidence is fragmented
SCADA, vibration, acoustic data, images, work orders, and operator notes rarely arrive in one view.
All use cases
For maintenance, reliability, safety, and plant operations teams
From a strange reading to a fix you can stand behind.
Qriton connects sensor data, inspection imagery, maintenance history, and operator knowledge into one diagnosis: what is failing, why, how severe it is, and how long the team has to act.
Discuss an industrial pilotAccountable decision path
EvidencePlant evidence
↓Qriton reasoningRoot-cause diagnosisUncertainty and policy visible
↓OutcomeMaintenance action
Evidence attachedHuman review readyReplayable
An anomaly alert is not a maintenance decision.
The difficult work begins after a threshold is crossed. Teams still have to reconcile conflicting systems, identify the cause, decide urgency, and justify the intervention.
Scores hide the cause
An equipment-risk number does not tell the team which failure modes are contributing or what to fix first.
The record is retrospective
After an incident, teams reconstruct why a decision was made from systems that captured only pieces of the story.
Replace reconstruction with a governed decision path.
The current workflow
- An alarm identifies a deviation
- Specialists gather evidence across systems
- Teams debate likely causes and severity
- The intervention record is assembled afterward
With Qriton
- Multimodal evidence enters one diagnostic path
- Failure patterns are composed into a readable cause
- Uncertainty routes ambiguous cases for review
- The approved action retains a replayable record
From operational evidence to accountable action.
Collect
Bring together time-series sensors, logs, notes, images, 3D data, and relevant safety context.
Diagnose
Evaluate equipment condition, contributing failure modes, severity, and remaining useful life.
Review
Show uncertainty and supporting evidence so specialists can challenge or approve the diagnosis.
Act
Translate the condition into an intervention window and a clear maintenance recommendation.
Replay
Keep the evidence, model version, reasoning path, review state, and final action together.
Make the answer useful to the people responsible for acting.
Identify the contributing failure patterns instead of returning only an anomaly.
Express remaining useful life in terms a maintenance team can schedule around.
Preserve why the part was repaired, monitored, or replaced.
Fit the system around the operational boundary.
Start from the evidence, policies, infrastructure, and human responsibilities already present in the workflow.
Keep plant data local
Run on customer-controlled infrastructure when operational signals cannot leave the site.
Work with existing evidence
Start from the sensors, maintenance systems, images, and operator processes already in place.
Keep people in the decision
Set review thresholds for uncertain, costly, safety-relevant, or irreversible actions.
The team receives a diagnosis, not another dashboard score.
Every recommendation stays attached to the evidence and actual reasoning that produced it. Maintenance can plan the work, safety can review the uncertainty, and an auditor can reopen the same decision later.
Explore Qriton DiagnoseWhat to establish before a pilot.
Which data can the diagnostic path use?
Time-series sensors, logs, work orders, operator notes, inspection images, 3D point clouds, and other relevant operational records can be combined according to the pilot scope.
Does Qriton replace existing monitoring systems?
No. The use case begins with the evidence those systems already produce and adds a governed diagnostic and decision layer.
How are uncertain diagnoses handled?
Uncertainty remains visible and can trigger specialist review before an intervention is recommended or executed.
Can the system run inside the plant environment?
Yes. Qriton is designed for customer-controlled and edge deployment where confidentiality, connectivity, or latency makes remote inference unsuitable.
Start with one asset and one expensive failure question.
Bring the available evidence, known failure modes, and intervention constraints. We’ll map the smallest diagnostic pilot that can produce a useful decision.