Detect anomalies in application metrics

Automatic anomaly detection in application and system metrics helps to identify issues in the early stages and prevent failure propagation. We have designed specialized anomaly detection algorithms for AIOps environments, where metric patterns tend to change constantly because of application upgrades and user base expansion.


Receive alerts before failures

Our solutions are designed to continuously score ongoing metrics so that the operations team can be notified about anomalous situations before they develop into major failures. This is achieved by continuously calculating anomaly likelihoods and applying adaptive thresholding logic to convert likelihood scores into alerts.


Simplify root cause analysis

Anomaly detection is only one stage in a complex process that also includes issue investigation and troubleshooting. We provide tools that analyze anomaly counts and densities to identify plausible root causes that operations teams can investigate further. This reduces both reaction times and labor costs.


Easily add new metrics

Our AIOps platform is designed to scale as new applications, systems, or metrics are added or removed. New entities can be added in runtime by uploading new configurations.


Immediately track new metrics

The platform provides several strategies for onboarding new metrics and entities. You can choose between accumulating sufficient ongoing data and training a new anomaly detection model or using an existing model for entities of the same type. This helps to immediately track new metrics whenever possible, reducing onboarding time and complexity.


Easily calibrate the system

Anomaly detection solutions need to be calibrated to avoid excessive alerts. Our AIOps platform comes with calibration tools that can learn from feedback provided by operations teams to find the optimal balance between the number of false positives and negatives.

Use Cases

Our clients

Finance & Insurance

How to get started

We provide flexible engagement options to help you build AIOps solutions faster. Contact us today to start with a workshop, discovery, or proof of concept (POC).
The Essential Guide to Transforming IT Operations with AIOps

Modern IT operations have to deal with dynamic mixes of public cloud platforms and services, cloud-native and serverless applications, and on-premise deployments. These systems, services, and applications generate enormous amounts of data that are challenging to collect, analyze, and use for issue detection and remediation. In this white paper, we discuss how this challenge can be addressed using machine learning and artificial intelligence methods, what aspects of IT operations can be improved using such techniques, and how companies should plan their capability roadmaps in this area. 

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