Reduce MTTR

Reduce MTTR with AI-Correlated Logs and Metrics

Everyone defines MTTR a little differently, but it all comes down to fixing problems and freeing up time to innovate. Whatever it is, fix it fast with LM Envision and AI-powered correlation.

Try it free

Reduce MTTR with modern monitoring and proactive data

Accelerate troubleshooting by combining logs, metrics, and AI-driven insights, enabling teams to swiftly identify root causes and minimize downtime.

Key Benefits:

Unified Logs and Metrics: Centralize all observability data to eliminate tool sprawl and streamline incident response.
AI-Powered Anomaly Detection: Leverage machine learning to detect anomalies in real-time, reducing noise and highlighting critical issues.
Contextual Root Cause Analysis: Correlate alerts with relevant log data to quickly pinpoint and resolve underlying problems.

90%

Less alert noise

Built to reduce MTTR at every layer

Eliminate guesswork and resolve issues faster with predictive and proactive observability

Unified logs and metrics

Proactive AIOps

Automated correlation

Intelligent log analysis

Historical data access

Access the complete picture for faster resolution

Ingest logs and metrics from every part of your stack—cloud, network, infrastructure, apps—into one platform. View log lines alongside alert timelines and resource health without switching tools or losing context.

Detect issues early with AI-driven signals

Log-based anomaly detection analyzes events at ingest—correlating patterns across cloud services, systems, and K8s pods—to surface performance degradations early and route them to the right Ops teams before SLAs are at risk.

IT metric-based alerts connect dots between symptoms and sources

Access correlated log data to add context to alerts and reduce time identifying root cause.

Triage logs with real-time pattern recognition

Import log data to LM Envision for monitoring and analysis with integrations, modules, and pre-built templates.

Uncover long-term patterns and recurring issues

Access months of enriched logs and metrics to trace back event spikes, compare system behavior pre- and post-deployment, or analyze recurring latency patterns tied to specific services.