R Systems Launches EXIQO To Scale Agentic AI Across Enterprises

20 Mar 2026 CW Team

EXIQO is positioned as a people-led, platform-amplified solution that combines human oversight, enterprise context and embedded guardrails. It brings together 1,400+ AI-native engineers, R Systems' proprietary OptimaAI Suite and a governed delivery methodology to accelerate engineering velocity and deliver measurable outcomes.

The OptimaAI Suite is described as a unified platform for agentic business operations, software development life cycle acceleration and legacy modernisation, offering enterprise-grade guardrails, policies and a library of more than 150 digital agents alongside reusable connectors, prompts, evaluations and data models. The delivery methodology focuses on rapid onboarding, identifying high-impact use cases and building scalable, cost?efficient solutions.

Early deployments reportedly achieved measurable operational improvements when measured against predefined business key performance indicators in live enterprise environments, including 40–55 per cent uplift in productivity, up to 50 per cent reduction in support and operational overhead and 50–70 per cent automation in high-volume workflows, while enabling up to two times faster execution across teams. R Systems said EXIQO has been used to improve engineering throughput, reduce repetitive development effort and strengthen release quality and invited organisations to learn more via the product and corporate websites.

The company framed EXIQO as adaptable to existing enterprise systems, preserving institutional knowledge while allowing customers to choose underlying foundation models and maintain compliance. R Systems highlighted its partner ecosystem and culture of co-creation as supporting sustained strategic growth for clients adopting the platform.

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R Systems International Limited has launched EXIQO, an AI Studio designed to enable enterprises to scale production-grade agentic AI across business and technology functions. The company said the move responds to industry barriers to adoption after research produced by Everest Group for R Systems found only 15 per cent of enterprises have operationalised agentic AI at scale, with integration complexity, governance risks, legacy system constraints and talent readiness gaps cited as primary obstacles. EXIQO is positioned as a people-led, platform-amplified solution that combines human oversight, enterprise context and embedded guardrails. It brings together 1,400+ AI-native engineers, R Systems' proprietary OptimaAI Suite and a governed delivery methodology to accelerate engineering velocity and deliver measurable outcomes. The OptimaAI Suite is described as a unified platform for agentic business operations, software development life cycle acceleration and legacy modernisation, offering enterprise-grade guardrails, policies and a library of more than 150 digital agents alongside reusable connectors, prompts, evaluations and data models. The delivery methodology focuses on rapid onboarding, identifying high-impact use cases and building scalable, cost?efficient solutions. Early deployments reportedly achieved measurable operational improvements when measured against predefined business key performance indicators in live enterprise environments, including 40–55 per cent uplift in productivity, up to 50 per cent reduction in support and operational overhead and 50–70 per cent automation in high-volume workflows, while enabling up to two times faster execution across teams. R Systems said EXIQO has been used to improve engineering throughput, reduce repetitive development effort and strengthen release quality and invited organisations to learn more via the product and corporate websites. The company framed EXIQO as adaptable to existing enterprise systems, preserving institutional knowledge while allowing customers to choose underlying foundation models and maintain compliance. R Systems highlighted its partner ecosystem and culture of co-creation as supporting sustained strategic growth for clients adopting the platform.

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