IR's New AI Unlocks Decades of Data in High-Stakes 'Nonstop' Systems

SYDNEY, Australia – June 17, 2026 – In a significant move to bridge the gap between legacy mission-critical systems and modern artificial intelligence, Integrated Research (ASX: IRI) today launched Iris for HPE Nonstop. The new product embeds a conversational AI layer directly into the systems that power a vast portion of the world's financial transactions, retail operations, and telecommunications networks. This launch represents a critical commercialization milestone, moving beyond theoretical AI applications to solve a tangible, high-stakes business problem: unlocking the complex data within the world’s most resilient computing environments.

For decades, HPE Nonstop systems have been the unsung heroes of the global economy, prized for their 100% fault tolerance and continuous availability. However, this reliability has come with a trade-off: immense complexity. Managing these platforms has traditionally required a small, highly specialized group of engineers, creating data silos and a growing knowledge gap. IR's launch of Iris aims to dismantle these silos, translating complex system telemetry into plain-language conversations and, in doing so, turning a decades-old platform into a modern, AI-augmented asset.

The Challenge of 'Locked-Up' Expertise

The core problem Iris for Nonstop addresses is one of access and expertise. HPE Nonstop environments are data-rich, containing invaluable real-time information on everything from payment authorizations to stock market trades. Yet, this data has been notoriously difficult for broader IT teams, let alone business executives, to access and interpret. As IR's CEO Ian Lowe noted in the announcement, "the data that keeps these environments running has traditionally been locked up in specialist tools and expertise."

This 'locked-up' data creates significant operational friction. When an issue arises in a system where downtime is measured in millions of dollars per minute, the time it takes to diagnose the root cause is critical. Without a democratized way to query system health, organizations are entirely dependent on a handful of specialists, leading to slower Mean Time To Resolution (MTTR). Furthermore, the challenge is compounded by a demographic shift, with an aging support staff and difficulty attracting new talent to manage these legacy platforms. This creates a precarious situation for companies that have invested heavily in Nonstop infrastructure and depend on it for core business functions.

Industry analysts have pointed out that simply exposing raw data from a legacy system to a generic AI platform is insufficient. Without a deep, contextual understanding of the environment's unique architecture, workloads, and business logic, AI-driven insights can be mediocre at best. IR's strategy directly confronts this by training its AI specifically on the intricacies of the Nonstop ecosystem, a crucial step in delivering tangible value.

Democratizing Data with Conversational AI

Iris for Nonstop acts as an intelligent translator, sitting atop IR's long-standing Prognosis monitoring platform. It allows any authorized user—from a junior IT operator to a business line manager—to ask complex questions in natural language and receive immediate, context-aware answers. An operator can now simply ask, "Is CPU usage normal for this time period?" or "Can you show me the network traffic trends over the past 2 weeks?" and receive not just data, but explanations and recommended next steps.

This capability is a game-changer for incident resolution. By synthesizing real-time telemetry from Prognosis into guided insights, Iris helps teams pinpoint root causes faster. It effectively lowers the barrier to entry for managing one of the most complex IT environments in the enterprise. "With Iris for Nonstop, we're providing AI powered intelligence direct to the IT function," Lowe stated. "Iris understands Nonstop, understands context unique to each clients environment, and can turn complex telemetry into actionable insight in seconds."

Beyond reactive troubleshooting, the platform supports proactive management. By leveraging the full suite of Prognosis capabilities, Iris can identify trends in system capacity and batch processing workloads, enabling teams to plan ahead and prevent performance degradation before it impacts production. This shift from reactive to proactive management is a key tenet of modern IT operations and a direct path to improving a company's bottom line.

Navigating a Crowded AIOps Landscape

Integrated Research is not entering an empty field. The market for AI in IT Operations (AIOps) is booming, with tech giants and specialized vendors alike racing to offer intelligent solutions for managing complex IT estates. Major players, including HPE itself with its OpsRamp platform and the recently announced partnership between IBM and ServiceNow, are focused on applying AI to unlock data from legacy systems. The industry consensus is clear: natural language assistants and agentic workflows are rapidly becoming table stakes for observability platforms.

In this competitive environment, IR's commercial strategy hinges on its deep specialization. While many competitors offer broad, horizontal AIOps platforms, IR is leveraging its 30-year history and established customer base within the Nonstop community. Its key differentiator is not just AI, but AI that is purpose-built for a specific, high-value niche. This focused approach allows Iris to provide a level of contextual understanding that general-purpose tools struggle to match, a critical advantage when dealing with mission-critical infrastructure.

This strategy of targeting an existing, loyal customer base with a high-value add-on is a classic and effective commercialization path. By making Iris available as part of the Prognosis 13.3 release, IR is providing a direct upgrade path for clients already invested in its ecosystem.

The Commercialization Path: From Core to Edge

Ultimately, the success of Iris will be measured by its ability to help customers extract more value from their substantial investments in Nonstop technology. As companies embrace hybrid strategies—running Nonstop in traditional data centers, virtualized environments, and at the network edge—the complexity of management explodes. A unified intelligence layer becomes not a luxury, but a necessity.

As Lowe explained, "Our clients are running Nonstop everywhere... 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." This unified view is essential for maintaining performance and availability across a distributed landscape.

For the financial institutions, retailers, and telecommunication firms that form the backbone of IR's clientele, this innovation translates directly into risk mitigation and operational efficiency. By accelerating incident resolution and enabling proactive planning, Iris helps ensure the continuous service delivery that their customers expect and their business models demand. This launch is a clear example of how targeted AI can extend the life and enhance the value of critical legacy infrastructure, proving that even the most established enterprise technologies can find new profitability in the age of artificial intelligence.