Exploring AIOps with Selector AI
Cloud Field Day 22 is digging deep into network observability, and Selector AI took the stage to share how their innovation is particularly instructive in demonstrating AI’s impact on IT infrastructure management. Deba Mohanty, Sachin Natu, and John Heintz were on hand to introduce Selector and walk us through how Selector’s platform is delivering new capability to network observability.
Deba started with an introduction of where Selector AI fits within the world of infrastructure management. Selector software sits above network data and captures insights regarding event correlation and root cause analysis, network language models, and digital twin technologies. TechArena previewed the concept of their network language model in our preview blog, as it reframes what network administrators can do to simplify oversight. Deba described the complexity cleanly, describing admins moving between dozens of network, infrastructure, and application dashboards, today to root cause issues and glean insights about the state of the enterprise environment. He stated that Selector is obsessed with simplifying this state of affairs, utilizing their tool suite.
What’s the outcome? Deba shared that customers across telco, retailers, broadcast, and finance sectors are targeting Selector AI tools for data center, network backbone, and edge environments. They’ve built a model that is a Slack native interface, designed for mobile optimization for delivery of alerts and prompts for network management. John walked us through a demo of Selector in action. He showed a SelectorAI “smart ticket,” stating that a ticket provides more insight to an admin with integrated action buttons for simple execution of actions. Admins can choose to take action or look for more information based on an AI chat app. From the chat app, admins can view topology maps with color coding for simplified views of network status. In the example, John showed two telco services, Verizon and AT&T, with degraded service (orange) within the Verizon service. The interface provides easy click-down for even more context about a particular service or network function. The dashboard is created dynamically, based on data relevant to the ticket with further drilldown, showcasing broader data that may be useful to the admin. Selector clarified that this real time work is deep data analytics vs Gen AI, as there is no benefit for LLM digesting raw network data. Selector is layering a RAG implementation to take LLM model of customer choice to drive recommendations.
What’s the TechArena take? Integration of Gen AI into observability to help drive better and more efficient actions makes excellent sense. However, the large providers have announced that they’re working on this capability across the full network management realm. We’ve covered this on TechArena in the past, for example, with Juniper’s Mist AI solution, and Marvis/Marvis Minis for wired and wireless network management. In the observability space, NetScout, DataDog, Dynatrace, and others have announced AI integration of some sort. So where does this leave a relatively new entrant like Selector AI? In the world of RAG model implementation, we expect that Selector may become an acquisition target for larger observability players to obtain differentiated IP for maximum market impact. Regardless, if Selector achieves market traction more organically, or through gaining M&A interest, this is a trend for network administrators to have on their radar and a company that could deliver differentiated capability.
Cloud Field Day 22 is digging deep into network observability, and Selector AI took the stage to share how their innovation is particularly instructive in demonstrating AI’s impact on IT infrastructure management. Deba Mohanty, Sachin Natu, and John Heintz were on hand to introduce Selector and walk us through how Selector’s platform is delivering new capability to network observability.
Deba started with an introduction of where Selector AI fits within the world of infrastructure management. Selector software sits above network data and captures insights regarding event correlation and root cause analysis, network language models, and digital twin technologies. TechArena previewed the concept of their network language model in our preview blog, as it reframes what network administrators can do to simplify oversight. Deba described the complexity cleanly, describing admins moving between dozens of network, infrastructure, and application dashboards, today to root cause issues and glean insights about the state of the enterprise environment. He stated that Selector is obsessed with simplifying this state of affairs, utilizing their tool suite.
What’s the outcome? Deba shared that customers across telco, retailers, broadcast, and finance sectors are targeting Selector AI tools for data center, network backbone, and edge environments. They’ve built a model that is a Slack native interface, designed for mobile optimization for delivery of alerts and prompts for network management. John walked us through a demo of Selector in action. He showed a SelectorAI “smart ticket,” stating that a ticket provides more insight to an admin with integrated action buttons for simple execution of actions. Admins can choose to take action or look for more information based on an AI chat app. From the chat app, admins can view topology maps with color coding for simplified views of network status. In the example, John showed two telco services, Verizon and AT&T, with degraded service (orange) within the Verizon service. The interface provides easy click-down for even more context about a particular service or network function. The dashboard is created dynamically, based on data relevant to the ticket with further drilldown, showcasing broader data that may be useful to the admin. Selector clarified that this real time work is deep data analytics vs Gen AI, as there is no benefit for LLM digesting raw network data. Selector is layering a RAG implementation to take LLM model of customer choice to drive recommendations.
What’s the TechArena take? Integration of Gen AI into observability to help drive better and more efficient actions makes excellent sense. However, the large providers have announced that they’re working on this capability across the full network management realm. We’ve covered this on TechArena in the past, for example, with Juniper’s Mist AI solution, and Marvis/Marvis Minis for wired and wireless network management. In the observability space, NetScout, DataDog, Dynatrace, and others have announced AI integration of some sort. So where does this leave a relatively new entrant like Selector AI? In the world of RAG model implementation, we expect that Selector may become an acquisition target for larger observability players to obtain differentiated IP for maximum market impact. Regardless, if Selector achieves market traction more organically, or through gaining M&A interest, this is a trend for network administrators to have on their radar and a company that could deliver differentiated capability.
