AI capabilities are increasingly used to manage the complexity of distributed, hybrid enterprise environments by improving analysis, correlation, and automation across interconnected systems. Traditional network monitoring based on siloed tools and static thresholds cannot keep up with the scale, speed, and interdependencies of modern infrastructure. Blurred boundaries between network, application, and infrastructure make root-cause isolation and operational resilience harder. AIOps platforms address the need for integrated observability, automation, and data-driven decisions. Selector AI presents an AIOps platform using a SaaS approach that includes professional services and supports co-development of customer platform instances. The platform provides full-stack observability with a data-centric architecture that ingests metrics, logs, configs, alerts, and topology into a unified analytics layer, correlating raw telemetry with ML to reduce alert fatigue and support hybrid and cloud environments.
"AI is becoming central to managing the growing complexity of distributed, hybrid enterprise environments, enabling more effective analysis, correlation, and automation across interconnected systems."
"Traditional infrastructure and specifically network monitoring approaches, often built around siloed tools and static thresholds, struggle to keep pace with the scale, velocity, and interdependencies of modern systems."
"Selector's strength lies in its data-centric foundation, ingesting diverse, multi-domain sources metrics, logs, configs, alerts, and topology into a unified analytics layer."
"Rather than positioning it purely as a product choice, it embraces the SaaS approach, considering professional services as part of the offering, coupled with the product features and encouraging a co-development approach towards the platform instance for customers."
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