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Nastel Announces AutoPilot Insight Real-User Monitoring

Nastel Technologies announced the addition of real-user monitoring and analytics to its flagship AutoPilot Insight software platform.

According to Charley Rich, Nastel’s VP-Product Management, “Slow Web apps are a terrific way to kill revenues, harm reputations and drive users to competitors. The problem is, even as traditional datacenter performance metrics say everything is fine, users are tapping their fingers with impatience because of sub-standard app responsiveness.

“AutoPilot’s new capabilities handle exactly this kind of situation, and can automatically pinpoint the source of problems that hurt a company’s reputation with its client base,” he said. “Basically, we capture and analyze two very different sets of data: the subjective user experience of fast or sluggish app responsiveness, and back-end server activities. Our secret sauce is being able to stitch together both data sets, analyze it, and deliver actionable insights to correct performance issues whenever and wherever they occur.

“The key to making real-user monitoring easy to deploy,” Rich continued, “is the use of browser-injection technology. So in addition to the detailed web and server metrics one would expect, our software enables clients to track end-user activities across geo-locations, and it automatically understands and visually depicts the relationship between application topologies and end-user requests.”

The ability to synthesize insights derived from topology mapping, server behaviors, and user requests—along with presenting probable root causes of problems in an intuitive visual manner—translates to reduced mean-time-to-repair (MTTR) of software issues and lower overall cost of support.

Whether a problem’s root cause is a JavaScript error on the client, network latency, or a slow Java method, AutoPilot Insight’s interface takes specialists to underlying problem issues with the press of a button. Detailed drill-down capabilities are provided in addition to single-click root-cause analysis.

AutoPilot Insight also stands apart from other solutions by offering natural language query capability that enables IT specialists to “talk” to data, enabling the detection of subtle, hidden patterns that enable solution of the toughest, most intractable performance problems.

Available key metrics include a full breakdown of page requests into all its components, browser-specific issues, geo-locations, top requests, worst response times, slowest loading pages, slowest server connections and much more.

“AutoPilot Insight,” Rich concludes, “is a unified solution that analyzes user requests, logs, metrics and transactions spanning the browser, web apps, middleware, brokers and mainframes. With this end-to-end measurement of performance you will rest easy that your users are satisfied and your company’s reputation is secure.”

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Nastel Announces AutoPilot Insight Real-User Monitoring

Nastel Technologies announced the addition of real-user monitoring and analytics to its flagship AutoPilot Insight software platform.

According to Charley Rich, Nastel’s VP-Product Management, “Slow Web apps are a terrific way to kill revenues, harm reputations and drive users to competitors. The problem is, even as traditional datacenter performance metrics say everything is fine, users are tapping their fingers with impatience because of sub-standard app responsiveness.

“AutoPilot’s new capabilities handle exactly this kind of situation, and can automatically pinpoint the source of problems that hurt a company’s reputation with its client base,” he said. “Basically, we capture and analyze two very different sets of data: the subjective user experience of fast or sluggish app responsiveness, and back-end server activities. Our secret sauce is being able to stitch together both data sets, analyze it, and deliver actionable insights to correct performance issues whenever and wherever they occur.

“The key to making real-user monitoring easy to deploy,” Rich continued, “is the use of browser-injection technology. So in addition to the detailed web and server metrics one would expect, our software enables clients to track end-user activities across geo-locations, and it automatically understands and visually depicts the relationship between application topologies and end-user requests.”

The ability to synthesize insights derived from topology mapping, server behaviors, and user requests—along with presenting probable root causes of problems in an intuitive visual manner—translates to reduced mean-time-to-repair (MTTR) of software issues and lower overall cost of support.

Whether a problem’s root cause is a JavaScript error on the client, network latency, or a slow Java method, AutoPilot Insight’s interface takes specialists to underlying problem issues with the press of a button. Detailed drill-down capabilities are provided in addition to single-click root-cause analysis.

AutoPilot Insight also stands apart from other solutions by offering natural language query capability that enables IT specialists to “talk” to data, enabling the detection of subtle, hidden patterns that enable solution of the toughest, most intractable performance problems.

Available key metrics include a full breakdown of page requests into all its components, browser-specific issues, geo-locations, top requests, worst response times, slowest loading pages, slowest server connections and much more.

“AutoPilot Insight,” Rich concludes, “is a unified solution that analyzes user requests, logs, metrics and transactions spanning the browser, web apps, middleware, brokers and mainframes. With this end-to-end measurement of performance you will rest easy that your users are satisfied and your company’s reputation is secure.”

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While 87% of manufacturing leaders and technical specialists report that ROI from their AIOps initiatives has met or exceeded expectations, only 37% say they are fully prepared to operationalize AI at scale, according to The Future of IT Operations in the AI Era, a report from Riverbed ...

Many organizations rely on cloud-first architectures to aggregate, analyze, and act on their operational data ... However, not all environments are conducive to cloud-first architectures ... There are limitations to cloud-first architectures that render them ineffective in mission-critical situations where responsiveness, cost control, and data sovereignty are non-negotiable; these limitations include ...

For years, cybersecurity was built around a simple assumption: protect the physical network and trust everything inside it. That model made sense when employees worked in offices, applications lived in data centers, and devices rarely left the building. Today's reality is fluid: people work from everywhere, applications run across multiple clouds, and AI-driven agents are beginning to act on behalf of users. But while the old perimeter dissolved, a new one quietly emerged ...

For years, infrastructure teams have treated compute as a relatively stable input. Capacity was provisioned, costs were forecasted, and performance expectations were set based on the assumption that identical resources behaved identically. That mental model is starting to break down. AI infrastructure is no longer behaving like static cloud capacity. It is increasingly behaving like a market ...

Resilience can no longer be defined by how quickly an organization recovers from an incident or disruption. The effectiveness of any resilience strategy is dependent on its ability to anticipate change, operate under continuous stress, and adapt confidently amid uncertainty ...

Mobile users are less tolerant of app instability than ever before. According to a new report from Luciq, No Margin for Error: What Mobile Users Expect and What Mobile Leaders Must Deliver in 2026, even minor performance issues now result in immediate abandonment, lost purchases, and long-term brand impact ...

Artificial intelligence (AI) has become the dominant force shaping enterprise data strategies. Boards expect progress. Executives expect returns. And data leaders are under pressure to prove that their organizations are "AI-ready" ...

Agentic AI is a major buzzword for 2026. Many tech companies are making bold promises about this technology, but many aren't grounded in reality, at least not yet. This coming year will likely be shaped by reality checks for IT teams, and progress will only come from a focus on strong foundations and disciplined execution ...

AI systems are still prone to hallucinations and misjudgments ... To build the trust needed for adoption, AI must be paired with human-in-the-loop (HITL) oversight, or checkpoints where humans verify, guide, and decide what actions are taken. The balance between autonomy and accountability is what will allow AI to deliver on its promise without sacrificing human trust ...

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