Selector AIOps is a game-changing full-stack observability platform that leverages AI/ML to solve the network and infrastructure visibility gap. Traditional observability solutions have failed to keep pace with the increasing complexity and scale of modern networks, leading to prolonged Mean Time To Resolution (MTTR) and operational inefficiencies. Selector AIOps provides actionable insights by correlating events across the entire IT stack – from network, infrastructure, and application layers – enabling enterprises to proactively identify, troubleshoot, and resolve issues, ultimately improving network uptime and business continuity. Selector, founded in 2019 with an AI/ML-first approach, recognized that existing observability tools were failing to provide adequate visibility in the networking space, leading to costly downtime for complex networks. Their founding team, with deep networking expertise from companies like Juniper and Google, aimed to fill this gap by building a solution from the ground up with AI and machine learning at its core, enabling rapid identification and resolution of issues.

The core technology of Selector leverages an ML-driven autocorrelation approach to correlate all telemetry across the full stack—network, application, infrastructure, and cloud—explicitly tying insights back to the network. This allows users to quickly answer critical questions like "was it the application, infrastructure, or network?" and provides actionable "who, what, when, where, how" details for resolution in seconds and minutes, not hours or days. The platform drastically reduces alert fatigue, automates incident response, and consolidates disparate tools into a true single pane of glass, reducing tool sprawl and technical debt. By democratizing access to data through AI, Selector eliminates the need for deep data science knowledge. This approach has led to significant market validation, with 80% of Selector's customer base now comprising Fortune 1000 companies, addressing challenges like data silos, tooling debt, and "escalation chaos" by significantly reducing the number of engineers required to address P1 incidents.

Selector emphasizes that simply applying generic AI tools to a data lake isn't sufficient; the data requires context and intelligence, and success depends on unifying networking/operations expertise with AI/ML engineering. Selector addresses this by integrating both skill sets within its team, ensuring purpose-built solutions that prevent "dirty data in, dirty outcomes out." While the platform offers extensive automation capabilities, including running e-books and workflows through partnerships with tools like Ansible and Puppet, the extent of full automation depends on customer readiness, often retaining human-in-the-loop for critical changes. Customers utilize Selector to build unified Network Management Systems (NMS), perform AI-driven root cause analysis, conduct synthetic monitoring with smart alerts, and leverage LLM-integrated textual data correlation. The platform also includes an "operational twin" for "what-if" analysis without impacting production and can be integrated into CI/CD pipelines for testing changes before deployment.

Presented by Stephen Ochs, Head of Marketing. Recorded live at AI Field Day 8 in San Jose, California on May 13, 2026. Watch the entire presentation at https://techfieldday.com/appearance/selector-ai-presents-at-ai-field-day-8/or visit https://TechFieldDay.com/event/aifd8/ or https://Selector.AI for more information.