Leverages Grok’s representational memory to analyze and self-learn from vast, multidimensional data sets, identifying subtle patterns and correlations that traditional methods miss. As Grok processes more data, its AI continuously improves, grouping related events and logs into detections.
Uses Grok’s representational memory to analyze and self-learn from vast, multidimensional data sets, identifying subtle patterns and correlations that traditional methods miss.
Employed after associative clustering to add context, distinguishing between patterns to determine which groups require action. Over time, Grok learns to identify and prioritize critical detections.
Flexible runbook automation that models expert decision-making to triage, investigate and resolve issues before human intervention. With an intuitive drag-and-drop interface, operators can create and manage automations. Used in conjunction with AI Automation Pipeline.
Flexible runbook automation that models expert decision-making to triage, investigate and resolve issues before human intervention. Employs an intuitive drag-and-drop interface.
Serves as an early warning system and precedes clustering and classification in Grok’s AI architecture. Also employed for continuous analysis of real-time data streams to identify subtle variations and seasonal patterns.
Allows for quick ingestion of event and data streams without the need for complex algorithm development or toolkits. Eliminates time from data preparation, accelerating time to value by 6 weeks or more.
Grok uniquely generates a prioritized Automation Pipeline without the need for offline or manual analysis. By leveraging a recommendation engine, it identifies and ranks remediations based on impact and frequency, informing engineers whether automation or a problem ticket is needed.
Serves as an early warning system and precedes clustering and classification in Grok’s AI architecture.
Employed after associative clustering to add context, distinguishing between patterns to determine which groups require action. Over time, Grok learns to identify and prioritize critical detections, automatically surfacing incidents with relevant context and recommendations for action and automations.
Grok provides operators the ability to override recommendations and ‘label’ known detections so that Grok can improve its model. Grok learns from feedback to enhance decision-making.
Proprietary technology that enables quick ingestion of event and data streams without the need for complex algorithm development or toolkits. Grok rapidly converts raw data into a format for consumption by our ML models. With no data preparation or manipulation required, our customers accelerate time to value by at least six weeks.
Grok uniquely generates a prioritized automation queue without the need for offline or manual analysis. By leveraging a recommendation engine, it identifies and ranks remediations based on impact and frequency.
Grok provides operators the ability to override recommendations and ‘label’ known detections so that Grok can improve its model. Grok learns from feedback to enhance decision-making and adaptability over time.
Employs GenerativeAI to automatically deliver real-time summaries, probable root causes, and personalized, actionable recommendations for fixes.
Employs GenerativeAI to automatically to deliver real-time summaries, probable root causes, and personalized, actionable recommendations for fixes.
Leverages Grok’s representational memory to analyze and self-learn from vast, multidimensional data sets, identifying subtle patterns and correlations that traditional methods miss. As Grok processes more data, its AI continuously improves, grouping related events and logs into detections.
Flexible runbook automation that models expert decision-making to triage, investigate and resolve issues before human intervention. With an intuitive drag-and-drop interface, operators can create and manage automations. Used in conjunction with AI Automation Pipeline.
Serves as an early warning system and precedes clustering and classification in Grok’s AI architecture. Also employed for continuous analysis of real-time data streams to identify subtle variations and seasonal patterns.
Grok uniquely generates a prioritized Automation Pipeline without the need for offline or manual analysis. By leveraging a recommendation engine, it identifies and ranks remediations based on impact and frequency, informing engineers whether automation or a problem ticket is needed.
Employed after associative clustering to add context, distinguishing between patterns to determine which groups require action. Over time, Grok learns to identify and prioritize critical detections, automatically surfacing incidents with relevant context and recommendations for action and automations.
Proprietary technology that enables quick ingestion of event and data streams without the need for complex algorithm development or toolkits. Grok rapidly converts raw data into a format for consumption by our ML models. With no data preparation or manipulation required, our customers accelerate time to value by at least six weeks.
Grok provides operators the ability to override recommendations and ‘label’ known detections so that Grok can improve its model. Grok learns from feedback to enhance decision-making and adaptability over time.
Employs GenerativeAI to automatically to deliver real-time summaries, probable root causes, and personalized, actionable recommendations for fixes.
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