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Selector AI is an AI-powered network observability platform that unifies signals across multi-domain environments to provide intelligent outcomes and root cause analysis (RCA). The platform helps networking and infrastructure operation teams lower their Mean Time to Repair (MTTR) and improve operational efficiency by addressing the challenges of fragmented data and tool sprawl. Through a three-layered approach of collection, correlation, and collaboration, Selector AI transforms raw telemetry into actionable insights, delivered through common communication tools like Slack and Microsoft Teams.
The presentation emphasizes that modern enterprises are overwhelmed by data silos and dashboard spiral, often utilizing 12 to 15 different observability tools that produce thousands of disconnected alerts. Reza Koohrangpour explains that Selector AI solves this by employing a horizontal data lake architecture that is vendor and domain-agnostic. Unlike traditional systems that use Extract, Transform, Load (ETL), Selector uses an Extract, Load, Transform (ELT) model. This approach preserves vital timestamps and source context, which is critical for correlating events across different domains, such as networking, cloud, and applications, to ensure that engineers see a unified timeline of an incident rather than fragmented pieces of a puzzle.
The platform’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. A standout feature is the integration of a Large Language Model (LLM) via Copilot, which allows operators to perform root cause analysis using plain English queries. Varija Sriram highlights that successful deployment relies on a Customer Success model, where Selector AI collaborates with clients to map metadata and business logic, ensuring the AI reduces noise and provides explainable results rather than black box answers.
Personnel: Reza Koohrangpour, Varija Sriram
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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
Watch on YouTube
Watch on Vimeo
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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