
Selector AI announced its Spring 2024 release. Innovations include a GenAI-powered network-oriented LLM, native full-stack monitoring capabilities, and event correlation with root cause analysis.
A unified monitoring, observability, and multi-domain AIOps platform – Selector, provides a single pane of glass with key functionality that historically required multiple tools.
With this release, Selector can not only act on top of existing tools, but can directly collect configuration, metric, event, and log telemetry with its native monitoring capabilities.
"Operations teams have long struggled with tool sprawl in their pursuit of actionable monitoring and observability," said Kannan Kothandaraman, CEO at Selector. "Today's release enables organizations to drive efficiency through tools consolidation while providing insights into the health and performance of their network and IT environments through Selector's innovations in AI and ML."
Key highlights of the new release include:
- Integrated GenAI: Leverage Selector Copilot to instantly access more meaningful incident summaries, assist with troubleshooting, and automate remediation.
- Monitoring and Observability: NetOps, DevOps, and SRE teams can now collect and analyze telemetry from the network up to their applications and everything in between.
- Root-Cause Analysis: An innovative causal approach identifies the root cause of an incident, fast-tracking the investigation and remediation of incidents.
- Digital Twin: With Selector, users can build and leverage digital representations of their network and IT infrastructure.
- GCP Marketplace: Selector is coming to the GCP marketplace in Q2, providing customers with frictionless software procurement, simplified vendor management, streamlined pricing, and the ability to burn down cloud commits.
"Gartner reports that, by 2027, more than 40% of operational activities will be performed using tools enhanced by GenAI, dramatically reducing the labor involved," said Nitin Kumar, CTO at Selector." Assistive troubleshooting, automated incident remediation, and incident summarization are key features that operations leaders have been demanding for years."
The Latest
Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...
Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...
The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...
The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...
In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...
AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.
The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...
The gap is widening between what teams spend on observability tools and the value they receive amid surging data volumes and budget pressures, according to The Breaking Point for Observability Leaders, a report from Imply ...
Seamless shopping is a basic demand of today's boundaryless consumer — one with little patience for friction, limited tolerance for disconnected experiences and minimal hesitation in switching brands. Customers expect intuitive, highly personalized experiences and the ability to move effortlessly across physical and digital channels within the same journey. Failure to deliver can cost dearly ...
If your best engineers spend their days sorting tickets and resetting access, you are wasting talent. New global data shows that employees in the IT sector rank among the least motivated across industries. They're under a lot of pressure from many angles. Pressure to upskill and uncertainty around what agentic AI means for job security is creating anxiety. Meanwhile, these roles often function like an on-call job and require many repetitive tasks ...