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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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