Sharon AI, an Australian Neocloud provider, has signed a five-year strategic agreement with Rafay Systems to standardize how it orchestrates AI infrastructure across its expanding network of data centers. For customer support teams that rely on AI-driven tools, the deal signals a move toward more reliable, governed, and consistently available cloud infrastructure - the kind that keeps chatbots, analytics, and automation platforms running without interruption.
The agreement shifts Sharon AI from managing individual cluster deployments to a unified operating framework. Rafay's platform will serve as a centralized orchestration and operations layer across all Sharon AI Factory environments, handling provisioning, governance, monitoring, and multi-tenant access for accelerated computing workloads.
What the platform actually does
The Rafay platform abstracts the complexity of procuring and managing physical GPU infrastructure. It provides automation, observability, and secure multi-tenancy capabilities so that customers can access AI compute capacity without owning or operating the underlying hardware. The architecture is designed to support orchestration of up to 150,000 GPUs over the five-year term.
"Sharon AI is delivering the trusted infrastructure and operational capabilities required to help customers realize measurable value," said James Manning, Co-founder and Chief Executive Officer of Sharon AI. "Our agreement with Rafay gives us a standardized platform for orchestrating and managing AI infrastructure across our expanding footprint."
Why standardization matters at this scale
Running AI workloads across multiple locations and tenants creates operational friction. Without a centralized control plane, governance becomes inconsistent, utilization drops, and service delivery slows. Rafay's platform sits above the physical infrastructure and provides operational guardrails for both Kubernetes and virtual machine environments through a single interface.
Haseeb Budhani, Co-founder and Chief Executive Officer of Rafay Systems, said the challenge goes beyond raw GPU access. "Providers also need the orchestration, automation and governance capabilities required to transform that infrastructure into secure, reliable and production-ready AI services," he said. "Sharon AI is building a highly scalable AI infrastructure platform for customers across Asia-Pacific and beyond, and we are proud to provide an operational foundation that can support its continued growth."
Internal capability and service delivery
The agreement also covers development of Sharon AI's internal expertise around the Rafay platform. The goal is consistent operating practices across deployments, which the company expects will improve infrastructure utilization, strengthen reliability, and accelerate how quickly customers can access advanced AI capabilities.
For professionals working with AI for Customer Support, infrastructure reliability translates directly into fewer outages for the tools their teams depend on. When a support chatbot or ticket-routing model sits on governed, well-orchestrated infrastructure, response times stay predictable and escalations decrease.
Why this matters for customer support professionals
Customer support teams rarely touch GPU clusters or Kubernetes nodes. But they feel the consequences when underlying infrastructure fails. This agreement points toward a model where AI services - from automated response systems to sentiment analysis tools - run on infrastructure designed for consistent uptime and secure multi-tenant access. For support leaders evaluating AI vendors, asking whether providers use standardized orchestration platforms like Rafay can surface meaningful differences in reliability. Technical support specialists who want to understand how these infrastructure layers affect the tools they manage can explore the AI Learning Path for Technical Support Specialists.
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