Built-in Insights & Recommendations
Sophie enriches incidents with corrective insights and resolutions written in plain English, reducing MTTR by 45%.
Moving to the cloud and monitoring your new stack? Sophie AIOps takes the burden off your shoulders by protecting your emerging infrastructures from issues that affect the business. Focus on higher-level tasks by bridging the monitoring skill gap, mitigating the risk of a fragile infrastructure, and enabling your team to become proactive by predicting issues and producing insights. We got your back(end).
Loom Systems’ AIOps platform, Sophie, uses advanced patented machine-learning algorithms and Artificial Intelligence
to detect and notify you about issues in your hybrid cloud environments.
Migrating to Azure? Monitor your disparate environment directly from Azure's Log Analytics tool, Azure cloud or applications. Our AIOps solution acts as an intelligence layer on top of your Azure Log Analytics, and provides your team with resolutions and insights in plain English.
Loom's AIOps is the only solution that already has the resolution database for Azure incidents. >> Sophie AIOps Overview on Azure Marketplace
If AWS is your choice, AIOps will provide you with immediate insight into the uptime, performance, and availability of your applications and systems. Achieve quick diagnosis and resolving of applications and infrastructure issues before they impact your customers.
Sophie correlates all relevant events into one consolidated incident, reducing alert fatigue. Before an incident is created, it is measured against historical events, verifying that it is truly anomalous and not arising from seasonality.
Sophie enriches incidents with corrective insights and resolutions written in plain English, reducing MTTR by 45%.
By auto-detecting anomalies without the need to set any alerting rules or thresholds, Sophie empowers IT teams to become proactive about incidents that require their attention.
On-premise, cloud or in a hybrid environment, Sophie's technology ingests any data.
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