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.”
The Latest
I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...
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 ...