InsightFinder AI

Founded 2016

Predict IT infrastructure failures and identify root causes before outages impact users

Pricing model

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You can use InsightFinder to predict system outages and identify root causes across your infrastructure and AI applications. The service uses unsupervised machine learning to inspect metrics, logs, and traces without requiring manual thresholds or alert rules. You can correlate signals across hybrid cloud setups, monitor multi-agent AI workflows for model drift, and uncover data quality issues. When issues arise, an automated reliability assistant investigates upstream dependencies, provides causal explanations with citations, and suggests remediation actions like rollbacks or ticket creation directly in Slack or Teams.

What InsightFinder AI does

Analyze telemetry streams to detect emerging anomalies in real timeCorrelate metrics, logs, and traces into causal incident graphsTrack AI agent prompt responses and tool calls for performance driftGenerate incident reports with direct citations to underlying system dataTrigger remediation scripts with human-in-the-loop approval workflowsRedact sensitive PII and PHI data from operational logs automaticallyIdentify redundant and low-value monitoring alerts across systemsPredicts system incidents up to six hours before impactUnsupervised machine learning requiring zero static threshold setupDeterministic causal root cause analysis with evidence citationsMulti-agent tracing and evaluation for production AI modelsAutomated noise reduction cutting alert volume by up to ninety percentReal-time model drift and data quality degradation detectionInteractive reliability agent that suggests and executes rollbacks