Corvil Intelligence Hub can detect anomalies based on deviations from learned baseline, changes in temporal patterns.
Corvil Intelligence Hub makes it easy for individuals with no data science expertise to use machine learning algorithms for anomaly detection.
Corvil Intelligence Hub can detect anomalies based on deviations from learned baseline, changes in temporal patterns (such as phase, range and trend), predicted breach of key operational limits, and others. Corvil further enables correlation of detected anomaly alerts to reduce noise and provide more accurate, prioritized, actionable insight.
The algorithms are designed to be low touch and operate unsupervised, which enables users to apply them to the metrics of interest. Then Corvil’s real-time engine does the rest -- powering through high volume, velocity, time-series data generated by digital business transactions in real-time to:
Better Data, Better Insights
Algorithms leverage Corvil’s real-time, granular, precision-sequenced data to deliver better insights.
Algorithms can be applied to any type of business, customer, or IT-centric metric.
Algorithms delivers reliable results without constant manual adjustments.
Optimized for high volume, time-series data generated every nanosecond by digital business transactions.
Low Noise Alerts
Detected anomalies are automatically correlated, deduplicated, consolidated and filtered to minimize the noise.
Explore details with multi-dimensional analysis and effective data visualizations