Set up AIMS in minutes and forget about tedious maintenance. AIMS will automatically identify anomalies within just 2 weeks
Avoid alert storms. Get notified only when anomalies can cause a real performance impact
Early alerts of abnormal behavior cover 100x more data points than traditional solutions
Your business is cyclical and so is the load on your IT infrastructure. At the same time, the number of performance metrics is exploding with technology proliferation. As a result, manual configuration of alert thresholds is no longer fit for purpose.
AIMS AI learns the cyclical normal behavior of all performance metrics. This allows you to efficiently extend your monitoring scope to cover all your bases and avoid monitoring blind spots.
With AIMS Anomaly Alerts you get an early warning of important performance issues with their cascading impact across your tech stack and business services.
The Anomaly Alert shows each metric deviation, the system impacted, the moment it started, and the relative deviation vs expected behavior. In this way, you can easily understand both the cause and impact of the anomaly.
The AIMS Score eases the challenge of understanding the behavior of your business from a bird’s-eye view. It calculates the health of your entire IT stack, digital business performance, and individual components.
AIMS reveals intuitive insights by scoring all data sources, applications, and connected digital services.
Sunburst visualizes all connected systems and presents the status of every component. It is a fast way to easily navigate from a high-level overview to detailed views and understand the statuses of your IT components.
The AIMS dashboard gives you a complete overview of ongoing anomalies, stopped components, and errors.
The activity feed contains information about systems and applications connected, changes, anomalies, and other updates.
Automatically find dependencies, correlations, and the components that can be impacted by anomalies. Get to the root cause faster and avoid the ripple effect.
AIMS does not just take single anomalies into account. It uses correlation groups to cross-detect anomalies and evaluates the impact caused across systems and applications. It detects where the anomalous behavior started and forecasts the potential impact.
In this guide, we deep dive into the key components, variants, fundamental differences between approaches, and conclude with what you need to consider before implementing Anomaly Detection.
How can anomalies in complex IT systems be detected with no prior knowledge on the system itself? Find out more information about this ambitious project.
How do anomaly detection and machine learning impact the operating model of IT departments? Learn first-hand from industry-leading experts in this webinar replay.
