Nyansa unveiled two new applications that bring visibility and insight to problematic blind spots on the end points of the user access network – the client Wi-Fi connection and the broadband WAN. The new solutions include Voyance WAN and the Voyance Client Agent.
Detailed WAN and client data is now integrated into the Nyansa’s popular Voyance platform as new data sources. These data sources are uniquely analyzed and correlated with all other client transactions to deliver extraordinary insight into the user experience not found in any other system.
Nyansa now uses real client, network, application and WAN data for end-to-end correlation and root cause analysis of user performance across every aspect of a client connection.
From the moment a device/user connects to the Wi-Fi network, traversing access networks to accessing applications across WAN links, Voyance provides customers with a comprehensive real-time and historical view of the entire user experience on the network.
A discrete new application within the Voyance platform, Voyance WAN incorporates flow data directly from the routers to address these challenges. WAN data is analyzed and correlated with the other data sources, such as client, network service and application performance, within Voyance. This gives IT teams end-to-end insight into the entire user experience, from a client accessing the WLAN, traversing the access network and the WAN to accessing essential applications.
And because Voyance WAN leverages data flows from WAN router interfaces, customers now have more flexible deployment options, eliminating the need to install on premise data collectors, called Voyance crawlers, at every site.
Voyance WAN currently integrates NetFlow and cFlow data with future support for jFlow and sFlow protocols. Minute by minute, the Voyance WAN application constantly analyzes any instance of high WAN utilization on any link to determine what users and apps are the cause of poor WAN performance. Companies can now compare sites by high link utilization minutes, see repeat top culprits by site or WAN interface, and compare by peak app utilization traffic with related costs.
Unlike conventional WAN monitoring solutions that simply provide normal aggregate application usage, Voyance WAN identifies the percent of service provider bandwidth taken up at times of peak WAN link utilization.
When user performance problems occur, Voyance automatically pinpoints whether the root cause is related to an application, Wi-Fi, a network service, broadband congestion, or the client itself. Currently Voyance is the only solution to provide such a breadth of analytics within a massively scalable public service or private cloud platform.
Nyansa’s Voyance WAN also employs a cloud sourced AppID engine. Using machine learning technology, the AppID engine identifies applications by automatically mapping routing data to actual applications. This dynamic database is then shared within the Voyance platform so all customers benefit.
To proactively address last hop Wi-Fi connectivity issues experienced by problematic clients, Nyansa’s Voyance client agent provides a 360 degree view of the user experience over Wi-Fi networks without the expense of hardware sensors or the need for manual diagnostic tools.
Secure, configurable and deployed in minutes, the Voyance client agent works seamlessly with popular systems and device management platforms. The Voyance client agent collects system information as well as Wi-Fi stats based on what the client is seeing. IT teams can actively perform synthetic tests against default or configurable targets to measure a variety of metrics such as throughput, latency, packet loss and jitter.
Client data is fed seamlessly into the Voyance platform, where it joins the other data sources such as Wi-Fi metrics, protocol and app metrics from examining live packet data and syslog messages from network services, Analysis of client data surfaces insights and allows for faster device problem identification, performance comparisons, most affected clients, locations as well as proactive remediation recommendations.
Network managers can quickly compare the user experience for remote users versus on-site users with client devices using the same Wi-Fi drivers or easily determine why clients are making poor roaming decisions or staying sticky to a given Wi-Fi access point.
IT staff also gain direct access to a wide range of client information such as Wi-Fi driver, CPU and memory statistics, battery status, SSID scan data, RSSI levels, channel use and more.
The Voyance client agent is currently available for Apple Mac OS X devices with support for Windows and Android platforms in the future.
The Latest
In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...
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 ...