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Nastel Introduces AutoPilot Insight 2.0

Nastel Technologies announced the next-generation version of its flagship software platform, AutoPilot Insight 2.0.

According to Charley Rich, VP Product Management “The new release fuses predictive anomaly and machine learning capabilities, business transaction tracking that spans corporate firewalls, raw information handling and analytics speed, and the flexibility to operate across dynamic IT environments—from mobile to mainframe. It provides the broad array of capabilities needed by developers, IT admins, and business analysts for enterprise-grade operations intelligence and APM.”

Rich said building a solution that fully addresses today’s client requirements demanded two years of ground-up product re-engineering. “Customers universally remarked that they needed to find data outliers faster and sense problem conditions before they actually affect users. They also wanted more powerful end-to-end transaction tracking capabilities, with the ability to tie transaction performance to business outcomes.”

With this guidance, Nastel added new anomaly detection capabilities based upon proven open source machine learning algorithms, which the firm improved and extended into real-time “smart” analytics. Over time, AutoPilot Insight rapidly improves at predicting, sensing, and evaluating the exact nature of performance issues.

Transaction tracking also was improved, giving IT users a precise microscope to identify problems, quickly resolve issues, and tie transaction performance directly to business performance through business objective reports.

“Enhancing our grid processing technology,” Rich continued, “was absolutely necessary to analyze insights in-memory from fast data—high-velocity streaming information with short-term value generated from a large multitude of IT infrastructure devices and instrumentation sources. Potential use cases include almost any industry vertical where detected data outliers demand fast decisions and interventions. Some examples include actual or potential financial system security breaches, supply chain issues that potentially cascade rapidly into major delivery problems affecting customers…the list is almost endless.”

Rounding out the new release’s capabilities are innovative mobile-centric features. Enriched by complementary anomaly detection, machine learning and business objective capabilities, mobile analysts can understand app user behavior and satisfaction far faster, leveraging real-time insights to improve services quickly without the burden of writing mobile instrumentation and analysis code.

AutoPilot Insight is not just for IT technologists, Rich stresses. “Nastel improved ease-of-use and out-of-the-box functionality for everyone, including non-technical business users. By adding intuitive workflows plus wizards, use-case templates, and dashboard launch-pad features, the net result is an extremely fast and capable APM platform that serves the analysis and reporting needs of everyone from developers, IT admins, telecom managers, and business analysts.”

Rich concluded: “The time of an IT admin sitting in a datacenter staring at metrics from a simple mix of technology assets has passed. AutoPilot Insight 2.0 provides insights to a wide variety of users through an APM solution that fuses high-performance data handling, sophisticated real-time smart analytics, and the ability to operate across diverse IT technology stacks and components. With intuitive visualizations garnered from massive amounts of information, the day of APM infometrics has arrived.”

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Nastel Introduces AutoPilot Insight 2.0

Nastel Technologies announced the next-generation version of its flagship software platform, AutoPilot Insight 2.0.

According to Charley Rich, VP Product Management “The new release fuses predictive anomaly and machine learning capabilities, business transaction tracking that spans corporate firewalls, raw information handling and analytics speed, and the flexibility to operate across dynamic IT environments—from mobile to mainframe. It provides the broad array of capabilities needed by developers, IT admins, and business analysts for enterprise-grade operations intelligence and APM.”

Rich said building a solution that fully addresses today’s client requirements demanded two years of ground-up product re-engineering. “Customers universally remarked that they needed to find data outliers faster and sense problem conditions before they actually affect users. They also wanted more powerful end-to-end transaction tracking capabilities, with the ability to tie transaction performance to business outcomes.”

With this guidance, Nastel added new anomaly detection capabilities based upon proven open source machine learning algorithms, which the firm improved and extended into real-time “smart” analytics. Over time, AutoPilot Insight rapidly improves at predicting, sensing, and evaluating the exact nature of performance issues.

Transaction tracking also was improved, giving IT users a precise microscope to identify problems, quickly resolve issues, and tie transaction performance directly to business performance through business objective reports.

“Enhancing our grid processing technology,” Rich continued, “was absolutely necessary to analyze insights in-memory from fast data—high-velocity streaming information with short-term value generated from a large multitude of IT infrastructure devices and instrumentation sources. Potential use cases include almost any industry vertical where detected data outliers demand fast decisions and interventions. Some examples include actual or potential financial system security breaches, supply chain issues that potentially cascade rapidly into major delivery problems affecting customers…the list is almost endless.”

Rounding out the new release’s capabilities are innovative mobile-centric features. Enriched by complementary anomaly detection, machine learning and business objective capabilities, mobile analysts can understand app user behavior and satisfaction far faster, leveraging real-time insights to improve services quickly without the burden of writing mobile instrumentation and analysis code.

AutoPilot Insight is not just for IT technologists, Rich stresses. “Nastel improved ease-of-use and out-of-the-box functionality for everyone, including non-technical business users. By adding intuitive workflows plus wizards, use-case templates, and dashboard launch-pad features, the net result is an extremely fast and capable APM platform that serves the analysis and reporting needs of everyone from developers, IT admins, telecom managers, and business analysts.”

Rich concluded: “The time of an IT admin sitting in a datacenter staring at metrics from a simple mix of technology assets has passed. AutoPilot Insight 2.0 provides insights to a wide variety of users through an APM solution that fuses high-performance data handling, sophisticated real-time smart analytics, and the ability to operate across diverse IT technology stacks and components. With intuitive visualizations garnered from massive amounts of information, the day of APM infometrics has arrived.”

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AI observability serves several roles across an organization. AI engineers use it to build, evaluate, and improve AI systems. SREs use it to operate those systems reliably and efficiently in production, including managing their cost. Leaders need it to connect AI behavior and operational performance to business outcomes. The common need is a clear understanding of whether AI systems are delivering the intended result ...

Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...

Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...

When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...

In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...

Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...

AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...

Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...

For more than a decade, the default infrastructure play has been simple. Move more workloads to the cloud, retire on-premises gear and treat the data center as a cost to shrink. That logic held while public cloud offered near-limitless capacity at predictable prices, and AI demand is now testing both halves of that assumption ...

Most enterprise IT leaders can tell you exactly how many applications are running across their organization. They can surface a dashboard showing telecom spend by region, pull a report on SaaS license utilization, or call up a real-time view of mobile device inventory. The data is there, and IT leaders have largely delivered on the promise of visibility. Yet, in most organizations, that visibility has not translated into business value ...