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Selector AI Introduces Network Language Model

Selector AI announced a series of innovations, including a Network Language Model (NLM), enhanced digital twin capabilities, and programmable synthetics.

These advancements are designed to help businesses reduce operational complexity, resolve issues faster, and accelerate decision-making through AI-powered insights.

Selector AI's platform is a unified AIOps solution that consolidates monitoring, observability, and multi-domain operations into a single, easy-to-use interface, eliminating the need for multiple disjointed tools.

"With our new Network Language Model, businesses can now gain real-time, actionable insights using intuitive natural language processing, enabling teams to streamline operations, slash mean time to detection and resolution (MTTD/MTTR), and improve overall network reliability," said Nitin Kumar, Co-founder and CTO of Selector AI. "This release allows enterprises to leverage their data like never before, offering AI-driven analytics at their fingertips to help reduce downtime and enhance productivity."

Key Business Benefits of the New Release:

- Network Language Model (NLM): Empowering operations teams to make faster, data-driven decisions by correlating alerts with AI-driven insights from email notifications, maintenance logs, and other sources—minimizing false positives and improving alert accuracy. This reduces manual intervention and ensures critical issues are resolved faster.

- Enhanced Digital Twin Technology: Enable IT teams to predict network behavior through "What-If" scenarios, driving better risk management and faster problem resolution across all network layers. IT teams can now anticipate failures before they happen, improving uptime and customer satisfaction.

- Programmable Synthetics Sensors: Providing visibility into application performance and availability while seamlessly correlating this data with network infrastructure. These programmable sensors enable businesses to detect and resolve application performance issues before they impact end users, protecting revenue and ensuring a seamless user experience.

"Operational efficiency is critical to businesses facing increased outages and growing complexity," said Kevin Kamel, VP of Product Management at Selector AI. "With these new capabilities, businesses can proactively prevent downtime, mitigate customer-impacting issues, and ensure service reliability through advanced analytics and sophisticated device modeling."

To meet this increasing demand, Selector AI is expanding its global footprint, with new offices across the US, Canada, India, and Japan, ensuring it continues to deliver cutting-edge AI and machine learning solutions to IT operations teams worldwide.

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Selector AI Introduces Network Language Model

Selector AI announced a series of innovations, including a Network Language Model (NLM), enhanced digital twin capabilities, and programmable synthetics.

These advancements are designed to help businesses reduce operational complexity, resolve issues faster, and accelerate decision-making through AI-powered insights.

Selector AI's platform is a unified AIOps solution that consolidates monitoring, observability, and multi-domain operations into a single, easy-to-use interface, eliminating the need for multiple disjointed tools.

"With our new Network Language Model, businesses can now gain real-time, actionable insights using intuitive natural language processing, enabling teams to streamline operations, slash mean time to detection and resolution (MTTD/MTTR), and improve overall network reliability," said Nitin Kumar, Co-founder and CTO of Selector AI. "This release allows enterprises to leverage their data like never before, offering AI-driven analytics at their fingertips to help reduce downtime and enhance productivity."

Key Business Benefits of the New Release:

- Network Language Model (NLM): Empowering operations teams to make faster, data-driven decisions by correlating alerts with AI-driven insights from email notifications, maintenance logs, and other sources—minimizing false positives and improving alert accuracy. This reduces manual intervention and ensures critical issues are resolved faster.

- Enhanced Digital Twin Technology: Enable IT teams to predict network behavior through "What-If" scenarios, driving better risk management and faster problem resolution across all network layers. IT teams can now anticipate failures before they happen, improving uptime and customer satisfaction.

- Programmable Synthetics Sensors: Providing visibility into application performance and availability while seamlessly correlating this data with network infrastructure. These programmable sensors enable businesses to detect and resolve application performance issues before they impact end users, protecting revenue and ensuring a seamless user experience.

"Operational efficiency is critical to businesses facing increased outages and growing complexity," said Kevin Kamel, VP of Product Management at Selector AI. "With these new capabilities, businesses can proactively prevent downtime, mitigate customer-impacting issues, and ensure service reliability through advanced analytics and sophisticated device modeling."

To meet this increasing demand, Selector AI is expanding its global footprint, with new offices across the US, Canada, India, and Japan, ensuring it continues to deliver cutting-edge AI and machine learning solutions to IT operations teams worldwide.

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Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

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