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Selector AI introduces an AI-powered network observability platform designed to unify multi-domain signals into actionable root cause analysis (RCA). The platform focuses on reducing the Mean Time to Repair (MTTR) by addressing the alert storm and fragmented data silos that plague modern network operations centers (NOC). By employing a three-layered approach of collection, correlation, and collaboration, Selector AI transforms thousands of disconnected telemetry signals into a single, cohesive incident report.
The platform distinguishes itself through a horizontal data lake architecture that utilizes an Extract, Load, Transform (ELT) model, preserving critical context and timestamps across various domains such as cloud, SD-WAN, and infrastructure. During the demonstration, Varija Sriram illustrated a typical day in the life of a NOC operator using Selector’s ChatOps and Agentic Copilot features. When a financial application in AWS became unreachable from Tokyo, the platform correlated synthetic probes, SNMP data, and optical link degradation into a single Slack alert. This allowed the operator to visualize the specific failing hop (a cloud gateway router) and understand the business impact without needing to manually pivot between twelve or more disparate monitoring tools.
Technically, Selector AI leverages a Kubernetes-based microservices stack and a sophisticated causation model that separates simple correlation from true underlying causes. The integration of Gemini-powered Large Language Models (LLMs) allows users to query the system in plain English to receive summaries and recommended action plans, such as contacting specific service providers or triggering automated remediation workflows like port resets. The platform also offers bi-directional integration with ITSM tools like ServiceNow and Jira, ensuring that all AI-driven insights and manual operator notes are synchronized across the organization’s existing workflow management systems.
Personnel: Varija Sriram
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