InsightFinder Unified Intelligence Engine (UIE) platform leverages our advanced AI to seamlessly integrate and analyze diverse data sources, delivering actionable insights for optimal decision-making.

Our portfolio of patents showcases our commitment to innovation and leadership in the field of AI for DevSecOps.

Integrations

Our platform expertly integrates with leading observability solutions and open source monitoring tools, ensuring a comprehensive and versatile approach to achieving AI-driven self-healing and self-optimizing.

Customer Success Stories

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Incident Prediction

Leverage the power of AI to foresee and preemptively address system incidents, ensuring smooth operations and zero downtime.

Incident Investigation

Empower your team with powerful root cause analysis to quickly identify, analyze, and resolve system incidents, enhancing efficiency and minimizing impact.

Unified Health View

Gain a comprehensive overview of your system’s health with real-time monitoring data fusion and insight extraction, enabling informed decision-making and proactive management.

KubeInsight Proactive Autoscaling

Leverage predictive AI to proactively eliminate both service outages and resource waste.

UBL Integration

InsightFinder customers benefit from proven AI models that predict and help prevent IT incidents. InsightFinder prides itself in offering innovative, patented, and patent-pending machine learning algorithms and artificial intelligence to power our ground-breaking Unified Intelligence Engine (UIE).

Unsupervised Behavior Learning System and Method for Predicting Performance Anomalies in Distributed Computing Infrastructures

G R A N T E D

An unsupervised behavior learning system and method for predicting anomalies in a distributed computing infrastructure. The distributed computing infrastructure includes a plurality of computer machines. The system includes a first computer machine and a second computer machine. The second computer machine is configured to generate a model of normal and anomalous behavior of the first computer machine, where the model is based on unlabeled training data. The second computer machine is also configured to acquire real-time data of system level metrics of the first machine; determine whether the real-time data is normal or anomalous based on a comparison of the real-time data to the model; and predict a future failure of the first computer machine based on multiple consecutive comparisons of the real-time data to the model. Upon predicting a future failure of the first computer machine, generate a ranked set of system-level metrics which are contributors to the predicted failure of the first computer machine, and generate an alarm that includes the ranked set of system-level metrics. The model of normal and anomalous behavior may include a self-organizing map.

InisghtFinder Inc is the exclusive licensee of this patent.

UBL Integration

Unsupervised Behavior Learning System and Method for Predicting Performance Anomalies in Distributed Computing Infrastructures

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