Zero Incident Framework

Zero Incident Framework software enables the AIOps platform to intake real-time telemetry from all connected monitoring tools and infrastructure endpoints. It ingests diverse data types seamlessly, providing the necessary comprehensive visibility for subsequent, advanced machine learning analysis.

Insights derived from platform analytics are clearly displayed as widgets, offering both business and operational insights. Users can customize and arrange these widgets on dashboards, allowing floor engineers and managers to track activities and landscape views effectively.

ZIF features predictive analytics tools that utilize unsupervised machine learning to anticipate potential failures and performance degradation in applications or devices. By continuously learning from data patterns, the platform creates opportunity cards that prompt operations teams to take proactive action before disruptions occur.

The functionality uses unsupervised learning to process aggregated data from multiple external tools. It identifies unique, recurring patterns, clustering them into high-priority cases and drastically reducing alarm fatigue for the IT operations team.

The software automatically clusters correlated events and alerts as distinct cases, each assigned a unique ID for tracking. The case description clearly displays the probable root cause, enabling operators to quickly analyze the issue and take the appropriate actions according to established procedures.