Keysight Technologies introduced enhancements to the company's Nemo Device Application Test Suite.
This software-centric solution uses automation and artificial intelligence (AI) to enable wireless service providers and application developers to accelerate the assessment of smartphone users' real-world interactions with native applications.
"Service providers and mobile app developers need a reliable way to verify the real end-user experience of accessing over-the-top (OTT) applications from a smartphone connected to the cellular network," said Matti Passoja, Head of Nemo Wireless Solutions at Keysight. "Keysight combines a unique set of in-house software technology solutions to create an automated app test method that uses real applications to provide more accurate insights into the network performance, even under the most complex and dynamic circumstances."
Keysight leveraged AI, machine learning (ML), and automation, using data captured by a native mobile app (not simulated data traffic), to create the new device test app method. This delivers a more accurate assessment of an end-user's interaction with the same mobile app. The new application test automation method enables wireless service providers to rapidly optimize 5G network performance and deliver a greater quality of experience (QoE) for smartphone users accessing some of the world's most widely used OTT services and social media applications, including Facebook Messenger, Microsoft Teams, Snapchat, TikTok, and Zoom.
The new automated test app method is one of three complementary test methods available within Keysight's Nemo Device Application Testing Suite. Depending on the type of the mobile application and the key performance indicators (KPIs), a specific test method is used in combination with a companion Nemo field test solution. Nemo Testing Suite users receive a comprehensive, realistic, and flexible 5G network performance validation and end-user QoE assessment.
Keysight's Nemo test tools capture real measurement data in the field for real-time or post-process analysis. These test tools include; Nemo Outdoor 5G NR Drive Test Solution, Nemo Backpack Pro 5G In-Building Benchmarking Solution, and Nemo Network Benchmarking Solution.
The Latest
In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...
Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...
In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ...
Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...
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.