BigPanda's Pragmatic AI takes the overwhelming volume of context-rich incident data and surfaces actionable, plain-language insights for your team.

By predicting probable Root Cause Changes, BigPanda helps IT Ops teams stay on top of real-time incident triage and cut down on the time it takes to investigate an incident and resolve it.

Key Features

• AI-Generated Incident Analysis
• Root Cause Change Prediction

AI-Generated Incident Analysis

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Gradual Rollout

This feature is currently available through a gradual rollout. If you would like to enable this feature for your organization, please contact your BigPanda Account Manager.

BigPanda's AI-Generated Incident Analysis (AIA) leverages large language model AI to provide plain-language incident titles and detailed descriptions built from enriched, actionable incident data.

In environments where Automatic AI-Generated Incident Analysis has been enabled, the summary will automatically appear in the Incident Details pane after the initial delay. In environments where it has not been enabled, you can manually generate an incident summary by clicking the Click to perform AI Incident Analysis button.

You can also configure AIA to automatically share incident summaries with other teams and platforms.

BigPanda AI Incident Analysis leverages the OpenAI LLM, but the security of your data is our top priority. BigPanda sources its Generative AI language models using a version that protects the data shared externally.

Learn more about how to speed up incident triage with automatic analysis in the AI-Generated Incident Analysis documentation.

Root Cause Change Prediction

A significant portion of incidents and outages are caused by software and infrastructure changes. Identifying these changes quickly and accurately is a core goal and challenge of incident management.

Root Cause Changes (RCC) aggregates change data from all your change feeds and tools, including CI/CD, Change Management and Auditing. This complex data is then normalized, making sure your team leverages consistent information across the platform.

With this standardized, aggregated data, BigPanda uses an algorithm based on natural language processing and vector space models to compare changes to active incidents. Changes that are identified as potential root causes are surfaced right on the active incident where teams are working.

RCC can rapidly speed up the root cause investigation process by identifying potential causes right at incident detection.

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Unsupervised Autonomy

BigPanda's AI Engine is unsupervised in function and does not require training. It will run autonomously in the background as soon as relevant data is present. Unlike supervised AI, human interaction and consistent input are not required for its upkeep and efficacy.

Learn more about marking suspect changes as matches in the Remediate Incidents documentation, or dig into the logic that drives successful root cause changes in the Root Cause Changes (RCC) documentation.

Next Steps

Pragmatic AI accelerates and improves your incident correlation and root cause investigation.

Paired with the operational and system insights of BigPanda Analytics, Pragmatic AI can dramatically improve and hone your IT Operations. Leverage Analytics to dig deeper into patterns for specific tools or resources and identify system weaknesses that can be improved to prevent outages.

Read more about BigPanda analytics and reporting options in the BigPanda Analytics documentation.

Or, if you're ready to start reviewing automated suggestions, check out the Manage Alert Correlation or [Suggested Changes](https://docs.bigpa