Cleric

Automatically groups related alerts from multiple sources into coherent incidents, eliminating duplicate and redundant notifications. Uses correlation logic to identify alerts that represent the same underlying problem across different monitoring systems.

Automatically investigates incidents by correlating logs, metrics, and traces across the entire infrastructure stack to identify the underlying cause. Performs causal analysis without requiring manual investigation or domain expertise.

Analyzes how incidents in one service impact dependent services and downstream systems. Maps the blast radius of failures across the infrastructure.

Integrates with multiple observability platforms and data sources to create a unified view of infrastructure health. Normalizes and correlates data from different monitoring tools without custom development.

Correlates signals (logs, metrics, traces, events) across heterogeneous infrastructure components to identify patterns and relationships. Connects data from different monitoring systems, services, and layers of the stack.

Intelligently filters out non-actionable alerts and false positives to reduce alert fatigue. Uses AI to distinguish between critical issues and expected noise patterns.

Analyzes and correlates data from complex, multi-vendor infrastructure environments without requiring extensive custom configuration. Works across different monitoring tools, cloud providers, and technology stacks.

Automatically evaluates the severity and impact of incidents based on correlated signals and system state. Prioritizes incidents by business impact rather than alert count.

Reduces MTTR by automating triage and root cause analysis, enabling faster incident response. Provides actionable insights that allow on-call engineers to resolve issues more quickly.

Automatically enriches alerts and incidents with contextual information from logs, metrics, traces, and service topology. Provides on-call engineers with all relevant information needed to understand and resolve issues.

Learns patterns from historical alert data to improve future triage and filtering. Adapts to your specific infrastructure patterns and alert characteristics over time.

Automatically reconstructs the timeline of events leading to an incident by correlating logs, metrics, and traces. Shows the sequence of failures and their causal relationships.