Moogsoft develops AIOps technology that helps enterprise IT Ops and DevOps teams become faster, smarter and more effective. Moogsoft AIOps’ real-time machine learning algorithms help teams remediate issues that impact their customers’ experience by: reducing operational noise (alert fatigue) across your production stack; proactively detecting Incidents and correlating Events across your monitoring ecosystem; streamlining collaboration and workflow across teams and toolsets; and codifying knowledge to make operators smarter when encountering future Incidents.
Noise Reduction - Patented algorithmic and machine-learning techniques can identify significant events and ignore noise, without requiring users to develop and maintain complex and brittle systems of rules.
Event Correlation - Patented supervised and unsupervised machine-learning techniques can identify correlations between events to provide a holistic view of incidents across multiple data sources.
Collaboration - Based on the holistic view of incidents, different specialists can be invited automatically to collaborate on the diagnosis and resolution of a single incident, working within the same Situation Room and sharing information and insights.
Moogsoft Integration with Cherwell
- Algorithms from Moogsoft AIOps will automatically identify significant monitoring events and assemble them into clusters of related events, known as Situations. For each Situation, an Incident record will be created within Cherwell. Updates flow in both directions, to make sure that both Cherwell and Moogsoft AIOps are always showing the current state of the issue under investigation.
- While Moogsoft AIOps does not depend on a CMDB, where there is useful information available (e.g. business service mapping), this can be used to enrich the event stream and generate more meaningful Situations.