Selector AI’s New Innovations Mitigate Operational Complexity and Decrease MTTR

Selector AI, the leader in network and infrastructure AIOps, is unveiling new capabilities that reduce operational complexity while powering fast issue resolution and decision-making with AI. The new offerings—the industry’s first Network Language Model (NLM), enhanced digital twin capabilities, and programmable synthetics—enable enterprises to unlock the value of their data and supercharge productivity.

Selector AI’s platform uses a combination of AI, machine learning (ML), and large language model (LLM)-driven, self-serve analytics to provide instant access to actionable insights and reduce MTTR by up to 90%, according to the company. As a unified AIOps solution, the platform brings monitoring, observability, and multi-domain operations together in a singly, easy-to-use interface that helps cut down on tool sprawl.

"Operational efficiency is critical to businesses facing increased outages and growing complexity," said Kevin Kamel, VP of product management at Selector AI. "With these new capabilities, businesses can proactively prevent downtime, mitigate customer-impacting issues, and ensure service reliability through advanced analytics and sophisticated device modeling."

The latest innovations from Selector AI include:

• Network Language Model (NLM), which drives greater operational efficiency by correlating alerts with AI-powered insights from email notifications, maintenance logs, and other sources, reducing false positives, improving alert accuracy, minimizing manual intervention, and accelerating critical issue resolution
• Enhanced digital twin technology that affords IT teams the ability to predict network behavior and anticipate failures before they occur, enhancing uptime, risk management, problem resolution, and customer satisfaction
• Programmable synthetics sensors that offer deeper visibility into application performance and availability while correlating this data with network infrastructure, empowering enterprises to detect and resolve application performance issues before it impacts the end user

"With our new Network Language Model, businesses can now gain real-time, actionable insights using intuitive natural language processing, enabling teams to streamline operations, slash mean time to detection and resolution (MTTD/MTTR), and improve overall network reliability," said Nitin Kumar, co-founder and CTO of Selector AI. "This release allows enterprises to leverage their data like never before, offering AI-driven analytics at their fingertips to help reduce downtime and enhance productivity."

To learn more about Selector AI, please visit https://www.selector.ai/.