InsightFinder AI-Driven IT Observability Platform
AI-driven IT Observability – real-time unsupervised machine learning based anomaly detection, automatic root cause localization, and on-demand remediation playbook creation for enterprise-scale IT infrastructure, applications, and services.
InsightFinder’s IT Observability gives ITOps, DevOps, and SRE Teams complete multi-modal analysis and visibility – to predict and prevent incidents and outages.
IT Ops, DevOps, and SRE Teams need to identify potential problems quickly – before they create incidents that impact customers. InsightFinder’s AI-driven IT Observability platform delivers the visibility and insight needed to solve the hardest IT problems.
IT Observability uses InsightFinder AI's Unified Intelligence Engine to analyze log, metric, trace, and dependency graph data to deliver real-time anomaly detection, root cause analysis, incident prediction, and auto-remediation. Identify issues and root cause quickly to reduce MTTD and MTTR, and to ensure application and infrastructure reliability and performance.
AI-Driven IT Observability Platform Features
Precise Anomaly Detection
Apply real-time multi-variate analysis with threshold-less alerting for anomaly detection that is rapid, accurate, and relevant.
Root Cause Analysis
Automatically analyze incidents, metrics, logs, and traces in real-time. Identify root cause of IT incidents in minutes instead of hours, while reducing false alerts by 90%.
Incident Prediction
Unsupervised machine learning predicts incidents without labeled training data. Automatically learns predictive causal chains to predict incidents before they impact customers.
Auto-Remediation
Leverage InsightFinder AI’s incident prediction to automate incident response. Use real-time analysis and predictions to trigger alerts, initiate remediation, and automate incident workflows based on your current runbooks.
Log File Compression/PII Compliance
InsightFinder monitors log files in real-time, then passes anomalies to the integration platform. Log data are compressed by more than 90% without loss. In many systems, log files contain elements of personally identifiable information (PII). InsightFinder redacts log files so no PII is transmitted or stored – ensuring compliance and reducing costs.
Operation Co-pilot
The InsightFinder LLM-driven Operation Co-pilot generates root cause summaries and action recommendations in natural language.
Reduce the complexity of results and give your incident responders clear direction.
Dependency Graph
The Dependency graph provides a visualization of the logic relationships between different system components - a key inference hint for root causal analysis.
Service Map
InsightFinder's Service Map provides a single view of system performance at the instance level. Immediate view of health and performance of system components.
Key Capabilities of AI-Driven IT Observability
Real-time Anomaly Detection
Root Cause Analysis
Threshold-less and Customizable Alerting
Incident Prediction
Predictive Trend Analysis
Unsupervised Machine Learning
Automatic root cause localization
Insights Dashboard
Resource Hotspot/Bottleneck detection
Time-travelling Service Health Map
Proactive Kubernetes Autoscaling
Unified Health View
InsightFinder Co-pilot – LLM-based root cause summary and recommendations
Solutions
Unified Health View
Observe your entire IT system health in real-time with one central view across all services, applications, and infrastructure. Catch production issues caused by new releases before your customers are affected.
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Incident Investigation
Identify the root cause of IT incidents in minutes instead of hours and reduce false alerts by 75% – 90%.
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Incident Prediction
Deploy our purpose-built AI platform to empower you and your teams with hours of advance notice.
See how it worksEasy Integrations
InsightFinder AI’s anomaly detection, root cause analysis, and incident predictions integrate easily into the leading Observability platforms – bringing high-power AI-based analysis to your existing Observability and Monitoring environment.
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