Cross-signal correlation and intelligent insights help cut down MTTR and restore service faster
Find root causes faster with unified correlation across all your observability data. Logify360 provides step-by-step evidence trails and helps identify incident patterns. Kubernetes operations (scale, rollback, restart) available directly from the UI.
The pain points every engineering team faces
Hours spent sifting through logs, metrics, and traces across multiple systems. By the time you find the root cause, user impact has already escalated.
Every minute of downtime costs money and damages reputation. Traditional debugging methods keep MTTR high, especially during off-hours when experts aren't available.
Logs, metrics, and traces live in separate tools. Connecting the dots requires deep expertise and context switching between multiple dashboards.
Hundreds of alerts surface during incidents. Without intelligent correlation, teams chase false positives or symptoms instead of root causes, wasting precious time.
Not every on-call engineer has deep context about every service. When the expert isn't available, incidents take longer to resolve, increasing business impact.
Each engineer approaches debugging differently. Without standardized RCA processes, resolution times vary wildly, making SLA compliance difficult.
How intelligent root cause analysis transforms incident response
Intelligent correlation automatically collects and correlates data from logs, metrics, and traces across your entire infrastructure. No more switching between tools or manually connecting data points.
Get a unified view of your system health in seconds, not hours.
Advanced pattern analysis identifies anomalies, outliers, and patterns that human eyes might miss. Intelligent detection identifies issues before they become full-blown incidents.
Catch problems early and prevent incidents from escalating.
Intelligent correlation builds a visual causal graph showing relationships between events, services, and anomalies. Understand not just what failed, but why it failed and what it impacted.
See the full picture of system dependencies and failure chains.
Every root cause comes with a complete timeline showing the incident progression, step-by-step evidence trail, and confidence score. Know exactly why the system made its decision.
Build trust with transparent, explainable recommendations.
Get notified immediately when root causes are identified. Built-in dashboards show incident trends, MTTR improvements, and common failure patterns across your infrastructure.
Stay informed and learn from incidents to prevent future ones.
Pre-approved playbooks let you fix common issues with a single click. For complex incidents, intelligent analysis suggests remediation steps with clear instructions.
Reduce manual intervention and cut resolution time by 50%+.
Watch how AI-RCA identifies root causes across logs, metrics, and traces with automated analysis, evidence trails, and remediation suggestions.
Proven outcomes from real engineering teams
No. Intelligent pattern analysis works with sampled data. Our algorithms are designed to identify root causes even when some data points are sampled, as long as error logs and anomalies are preserved. The system focuses on signal patterns rather than exhaustive data volume.
Yes. Root cause analysis supports all major observability formats including JSON logs, Prometheus metrics, OpenTelemetry traces, Datadog, New Relic, CloudWatch, and many others. It automatically normalizes data across formats to build unified causal graphs.
Root cause analysis respects your data retention policies and compliance requirements. All analysis happens on data that's already in your observability platform, and you can configure retention windows to match your compliance needs. Root cause analysis results and evidence trails are stored separately with their own retention policies.
Absolutely. Root cause analysis provides full control over confidence thresholds, anomaly sensitivity, and correlation rules. You can override any recommendation, adjust parameters per service or environment, and customize playbooks. The system adapts based on your configurations to improve future recommendations.
Typically 2-3 minutes from incident detection to root cause identification with evidence. This includes data collection, correlation analysis, causal graph generation, and confidence scoring. Complex incidents involving multiple services may take 5-10 minutes.
Root cause analysis provides full transparency with evidence trails and confidence scores. If you disagree with a recommendation, you can override it and provide feedback. The system adapts based on corrections and improves over time. Confidence scores below 60% are flagged for manual review.
Root cause analysis works immediately with current data, but accuracy improves over time as it builds patterns of your infrastructure. For best results, we recommend 1-2 weeks of baseline data, though teams see value within days of deployment.
Yes. Root cause analysis can be deployed on-premise or in air-gapped environments. All processing happens locally, and no data leaves your infrastructure. We support both cloud-native and self-hosted deployments.
Discover how Logify360 helps you monitor and optimize your entire stack
See how root cause analysis can help your team resolve incidents faster. Get a personalized demo tailored to your infrastructure and use cases.
Cross-signal correlation and intelligent insights help cut down MTTR and restore service faster
Find root causes faster with unified correlation across all your observability data. Logify360 provides step-by-step evidence trails and helps identify incident patterns. Kubernetes operations (scale, rollback, restart) available directly from the UI.
The pain points every engineering team faces
Hours spent sifting through logs, metrics, and traces across multiple systems. By the time you find the root cause, user impact has already escalated.
Every minute of downtime costs money and damages reputation. Traditional debugging methods keep MTTR high, especially during off-hours when experts aren't available.
Logs, metrics, and traces live in separate tools. Connecting the dots requires deep expertise and context switching between multiple dashboards.
Hundreds of alerts surface during incidents. Without intelligent correlation, teams chase false positives or symptoms instead of root causes, wasting precious time.
Not every on-call engineer has deep context about every service. When the expert isn't available, incidents take longer to resolve, increasing business impact.
Each engineer approaches debugging differently. Without standardized RCA processes, resolution times vary wildly, making SLA compliance difficult.
How intelligent root cause analysis transforms incident response
Intelligent correlation automatically collects and correlates data from logs, metrics, and traces across your entire infrastructure. No more switching between tools or manually connecting data points.
Get a unified view of your system health in seconds, not hours.
Advanced pattern analysis identifies anomalies, outliers, and patterns that human eyes might miss. Intelligent detection identifies issues before they become full-blown incidents.
Catch problems early and prevent incidents from escalating.
Intelligent correlation builds a visual causal graph showing relationships between events, services, and anomalies. Understand not just what failed, but why it failed and what it impacted.
See the full picture of system dependencies and failure chains.
Every root cause comes with a complete timeline showing the incident progression, step-by-step evidence trail, and confidence score. Know exactly why the system made its decision.
Build trust with transparent, explainable recommendations.
Get notified immediately when root causes are identified. Built-in dashboards show incident trends, MTTR improvements, and common failure patterns across your infrastructure.
Stay informed and learn from incidents to prevent future ones.
Pre-approved playbooks let you fix common issues with a single click. For complex incidents, intelligent analysis suggests remediation steps with clear instructions.
Reduce manual intervention and cut resolution time by 50%+.
Watch how AI-RCA identifies root causes across logs, metrics, and traces with automated analysis, evidence trails, and remediation suggestions.
Proven outcomes from real engineering teams
No. Intelligent pattern analysis works with sampled data. Our algorithms are designed to identify root causes even when some data points are sampled, as long as error logs and anomalies are preserved. The system focuses on signal patterns rather than exhaustive data volume.
Yes. Root cause analysis supports all major observability formats including JSON logs, Prometheus metrics, OpenTelemetry traces, Datadog, New Relic, CloudWatch, and many others. It automatically normalizes data across formats to build unified causal graphs.
Root cause analysis respects your data retention policies and compliance requirements. All analysis happens on data that's already in your observability platform, and you can configure retention windows to match your compliance needs. Root cause analysis results and evidence trails are stored separately with their own retention policies.
Absolutely. Root cause analysis provides full control over confidence thresholds, anomaly sensitivity, and correlation rules. You can override any recommendation, adjust parameters per service or environment, and customize playbooks. The system adapts based on your configurations to improve future recommendations.
Typically 2-3 minutes from incident detection to root cause identification with evidence. This includes data collection, correlation analysis, causal graph generation, and confidence scoring. Complex incidents involving multiple services may take 5-10 minutes.
Root cause analysis provides full transparency with evidence trails and confidence scores. If you disagree with a recommendation, you can override it and provide feedback. The system adapts based on corrections and improves over time. Confidence scores below 60% are flagged for manual review.
Root cause analysis works immediately with current data, but accuracy improves over time as it builds patterns of your infrastructure. For best results, we recommend 1-2 weeks of baseline data, though teams see value within days of deployment.
Yes. Root cause analysis can be deployed on-premise or in air-gapped environments. All processing happens locally, and no data leaves your infrastructure. We support both cloud-native and self-hosted deployments.
Discover how Logify360 helps you monitor and optimize your entire stack
See how root cause analysis can help your team resolve incidents faster. Get a personalized demo tailored to your infrastructure and use cases.
Intelligent Investigation
Intelligent Investigation