Start with clean test data to model your system and train the detector algorithm.
Test Data Validation Module
AI-guided anomaly detection
Pinpoint subtle issues in your measurement data across hundreds of signals in seconds.
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Test data challenges
Hidden test errors waste time & money
Too much test data
With gigabytes of measurement data generated daily, overwhelmed engineers don't have time to validate the results.
Undetected errors
Wiring and sensor failures, configuration errors, and system defects can go unnoticed for months.
Schedule and budget delays
Measurement issues that linger can mean invalid data, costly retesting, and schedule delays that waste time and money.
Test Data Validation Module
Breakthrough Anomaly Detector algorithm finds more errors faster
- Find multiple error types across hundreds of channels in seconds.
- Discover issues in individual channels or complex multivariate issues.
- Quickly inspect and prioritise errors using an intuitive data visualiser.
- Industry-proven algorithm finds more than 95% of known errors.
Initial testing showed the Monolith Anomaly Detector found more than 90% of known errors in our test data. We’re able to identify and fix issues in minutes that previously took weeks to discover.
- Head of Cell Testing, Battery Manufacturer
How it works
AI-guided Anomaly Detector
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Inspect your test data
Apply the detector to your test results to find subtle measurement errors and defects in seconds.
Visualise and prioritise
Explore ranked anomalies with an interactive visualiser to prioritise corrective action.
Automate the process
Integrate with your test systems to automate the detection process to find issues faster.
Monolith resources
Learn more about AI-guided anomaly detection
