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IR Launches Iris for HPE Nonstop

Integrated Research (IR) announced the launch of Iris for Nonstop, extending its conversational AI intelligence layer to HPE Nonstop environments.

IR has embedded Iris directly into the Prognosis Platform for HPE Nonstop. This allows IT teams, business application stakeholders and more to ask questions in natural language and receive immediate, context‑rich answers about the health, performance and capacity of their Nonstop systems.

"Nonstop powers some of the world's most critical transactions, but the data that keeps these environments running has traditionally been locked up in specialist tools and expertise," said Ian Lowe, CEO at IR. "With Iris for Nonstop, we're providing AI powered intelligence direct to the IT function. Iris understands Nonstop, understands context unique to each clients environment, and can turn complex telemetry into actionable insight in seconds."

IR's Infrastructure suite, powered by Prognosis, has long helped clients monitor, troubleshoot and optimize the performance and availability of these environments with real‑time dashboards, alerting and automated reporting.

Iris for Nonstop builds on this foundation by adding a conversational AI layer that:

  • Answers complex questions in plain language – Operators can ask questions such as "Is CPU usage normal for this time period?" or "Can you show me the network traffic trends over the past 2 weeks?", and Iris will respond with explanations, context and recommended next steps.
  • Accelerates incident resolution – By synthesizing Prognosis' real‑time telemetry into guided insights, Iris helps teams identify root causes faster, reducing mean time to resolution in high‑stakes Nonstop environments.
  • Democratizes Nonstop expertise – Iris makes Nonstop performance and capacity data accessible to broader IT, business and executive stakeholders, with easy‑to‑consume natural‑language summaries and reports.
  • Supports proactive capacity and batch planning – By leveraging Prognosis Infrastructure, Business Insight and Batch Manager capabilities, Iris can surface trends in capacity, usage patterns and batch workloads, helping teams plan ahead before issues impact production.

As Nonstop clients adopt virtual Nonstop, cloud deployments and hybrid infrastructures spanning core and edge, the complexity of managing performance and capacity continues to grow. The combination of Prognosis Server on Nonstop, Prognosis Edge, and now Iris for Nonstop gives organizations a unified intelligence layer over their distributed, mission‑critical environments.

"Our clients are running Nonstop everywhere – in data centers, in virtualized environments and at the edge," said Ian Lowe. "By embedding Iris directly into our Infrastructure solutions, we're giving our clients an AI assistant that understands their topology, their workloads and their SLAs, wherever Nonstop is deployed."

Iris for Nonstop is available now with Prognosis 13.3, for clients using IR Infrastructure and the Prognosis Platform for HPE Nonstop. 

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IR Launches Iris for HPE Nonstop

Integrated Research (IR) announced the launch of Iris for Nonstop, extending its conversational AI intelligence layer to HPE Nonstop environments.

IR has embedded Iris directly into the Prognosis Platform for HPE Nonstop. This allows IT teams, business application stakeholders and more to ask questions in natural language and receive immediate, context‑rich answers about the health, performance and capacity of their Nonstop systems.

"Nonstop powers some of the world's most critical transactions, but the data that keeps these environments running has traditionally been locked up in specialist tools and expertise," said Ian Lowe, CEO at IR. "With Iris for Nonstop, we're providing AI powered intelligence direct to the IT function. Iris understands Nonstop, understands context unique to each clients environment, and can turn complex telemetry into actionable insight in seconds."

IR's Infrastructure suite, powered by Prognosis, has long helped clients monitor, troubleshoot and optimize the performance and availability of these environments with real‑time dashboards, alerting and automated reporting.

Iris for Nonstop builds on this foundation by adding a conversational AI layer that:

  • Answers complex questions in plain language – Operators can ask questions such as "Is CPU usage normal for this time period?" or "Can you show me the network traffic trends over the past 2 weeks?", and Iris will respond with explanations, context and recommended next steps.
  • Accelerates incident resolution – By synthesizing Prognosis' real‑time telemetry into guided insights, Iris helps teams identify root causes faster, reducing mean time to resolution in high‑stakes Nonstop environments.
  • Democratizes Nonstop expertise – Iris makes Nonstop performance and capacity data accessible to broader IT, business and executive stakeholders, with easy‑to‑consume natural‑language summaries and reports.
  • Supports proactive capacity and batch planning – By leveraging Prognosis Infrastructure, Business Insight and Batch Manager capabilities, Iris can surface trends in capacity, usage patterns and batch workloads, helping teams plan ahead before issues impact production.

As Nonstop clients adopt virtual Nonstop, cloud deployments and hybrid infrastructures spanning core and edge, the complexity of managing performance and capacity continues to grow. The combination of Prognosis Server on Nonstop, Prognosis Edge, and now Iris for Nonstop gives organizations a unified intelligence layer over their distributed, mission‑critical environments.

"Our clients are running Nonstop everywhere – in data centers, in virtualized environments and at the edge," said Ian Lowe. "By embedding Iris directly into our Infrastructure solutions, we're giving our clients an AI assistant that understands their topology, their workloads and their SLAs, wherever Nonstop is deployed."

Iris for Nonstop is available now with Prognosis 13.3, for clients using IR Infrastructure and the Prognosis Platform for HPE Nonstop. 

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AI is becoming the operating system of the enterprise. It acts as an invisible coordination layer that understands intent, connects systems, and executes work across complex SaaS environments. Previously, employees had to click through multiple systems — CRM, ERP, support tools, collaboration platforms — to complete a single task. Now, instead of navigating each application manually, they can simply state what they need to accomplish ...

In 2026, the cost of downtime or an outage is no longer just a technical inconvenience; it's a $600 billion wake up call for global businesses. As our digital ecosystems become  more interconnected, each touchpoint introduces new risks and multiplies the consequences when things go wrong. And the data is clear: aggregate downtime costs  for Global 2,000 companies have surged 50% since 2024, reaching a staggering $600 billion ...

Deloitte found that 74% of enterprises expect to deploy agentic AI solutions in the next 24 months. However, the rush to deployment is outpacing foundational work, though. Only 21% of enterprises have fully formed agent governance models in place. The result? AI agents deployed without guidance or governance begin to function as fragmented islands of complexity ...

Cloud spending is no longer viewed as a passthrough IT expense, but as a strategic financial lever that directly impacts innovation capacity, profitability and enterprise resilience, according to the CFO Cloud Cost Optimization Report from Azul ...

As AI moves from generating responses to performing actions, the need for trust increases exponentially. And as organizations enlist AI agents for increasingly sophisticated business processes, trust is going to be the single most important theme for spurring adoption. What can organizations do to build trustworthy AI agents? ...

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 ...