Automatically Identify Root Cause, In Plain English, Without Digging Through Logs
When critical business services go down, metrics can alert you to the issue, but identifying the root cause requires a time-consuming search through log files.
ScienceLogic Zebrium AI Log Analysis – integrated with New Relic – does the heavy lifting for you, automatically performing machine learning log analysis across your New Relic systems and presenting the results in your New Relic dashboard.
Diagnose Issues 10x Faster with Automated Root Cause Analysis
When an application breaks, it’s a race against time to get it back online. Customers are frustrated, and critical resources are pulled from other work. ScienceLogic Zebrium AI Log Analysis uses machine learning AI models to eliminate much of the searching and interpretation required to diagnose root cause from logs.
- Processes enormous volumes of log messages in real-time to identify the root cause with machine learning
- Dramatically reduce time to understand what is actually broken, indicates quickly where to begin troubleshooting and repair
- No manual training required – unsupervised machine learning produces results in less than 24 hours
Understand What is Happening, Even if You Don’t Speak Log
Because no two logs are alike, looking through logs is like deciphering a foreign language – even for experienced developers. Some words may look familiar, but each log has its own unique vocabulary and syntax.
- Get plain language root cause summaries based on a natural language model with Generative-AI that understands your operations
- Distill billions of log lines down to the few most salient to isolate root cause
- Visualize the most important keywords from logs that describe the root cause
Identify Unknown Unknowns Before They Cause Incidents
The complexity of modern applications makes it challenging for any person to know what could break at any given time.
With Zebrium AI Log Analysis for New Relic your team can catch new problems without searching and building complex queries.
- Identify unusual or novel issues and associated root causes, even when you or your monitoring tool don’t know what to look for.
- Correlate unusual behavior with recent changes and performance metrics so you can understand potential business or service impact.
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