Customer Use Cases and Product Demonstration with Selector AI

Selector AI’s presentation at Cloud Field Day 22 showcased real customer use cases demonstrating how its platform aids operators and management teams in making critical cloud-based business decisions. John Heintz, Global Systems Engineering Director, highlighted the platform’s ability to resolve critical issues rapidly, emphasizing its unique position in meeting the daily needs of operators and management. The presentation featured a recorded demo showcasing the platform’s functionality, including the creation of “smart tickets” that automatically summarize events, provide context, and suggest remediation actions. The demo further illustrated how these tickets integrate with collaboration tools like Slack, offering a streamlined workflow for incident management.

A key aspect of the demo involved the platform’s dynamic dashboarding capabilities. These dashboards are contextually driven, automatically generated based on the details of an alert, presenting relevant topology renderings, color-coded KPIs, and drill-down capabilities for deeper investigation. The presenter addressed audience questions regarding the dashboards’ dynamic generation, highlighting the utilization of JSON data from alerts to build visualizations on the fly, while emphasizing the possibility of customer customization. He also explained how the platform’s chat ops functionality allows users to interact with the system using natural language, eliminating the need for complex queries and streamlining the investigation process.

The demo also showcased Selector AI’s capacity to integrate various data sources, including cloud providers’ native monitoring services, and its ability to correlate events across different systems to pinpoint root causes. The presenter highlighted features like correlation graphs and a time-series DVR function, which allow users to visualize the sequence of events leading to an incident. Finally, the discussion addressed the platform’s architecture, emphasizing its scalability built on Kubernetes and the use of techniques like Kafka for efficient data processing, and the flexibility to deploy agents to collect data from various environments. This ability to ingest data from diverse sources, whether existing monitoring tools or directly from the target systems, represents a core strength of the Selector AI platform.