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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, 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 multiple disparate monitoring tools.
Selector AI’s technical core relies on a Kubernetes-based microservices architecture and a sophisticated AI/ML stack that distinguishes between simple correlation and true causation. The system uses self-supervised and unsupervised learning to establish baselines and detect anomalies across more than 300 telemetry sources, including active and passive synthetic probes. A standout feature is the integration of a Large Language Model (LLM) via a Copilot, which allows operators to perform root cause analysis and receive recommended remediation steps using plain English queries. The roadmap includes expanding visibility into AI workloads and GPUs, while the current platform offers bi-directional integration with ITSM tools like ServiceNow and Jira to ensure that all insights and manual notes are synchronized across the organization’s existing workflows.
Personnel: Reza Koohrangpour, Varija Sriram
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