Staff Software Engineer

Rackspace and Uniphore announce strategic partnership. Mar 10, 2026 San Antonio, TX, March 10, 2026 - Rackspace Technology(R)(NASDAQ: RXT), a leading hybrid multicloud and AI solutions company, today announced a strategic partnership with Uniphore, the Business AI leader backed by NVIDIA and AMD, to deliver the industry's first Infrastructure-to-Agents architecture, offered as an outcomes-based service. The partnership reflects a shared ambition to unlock $100 million in enterprise AI deployments as customers move from AI experimentation to production at scale, without sacrificing governance, security, or control. This is especially relevant for regulated industries where choice, security and sovereignty are non-negotiable requirements. Operationalizing AI in production remains a challenge due to the complexity of choices across the stack. By integrating Uniphore's Business AI Cloud with Rackspace's private cloud infrastructure, Rackspace will deliver a full-stack secure and governed AI private cloud that includes: advanced inferencing capable of running on both NVIDIA and AMD compute architectures; Data Preparation-as-a-Service; fine-tuned Small Language Models (SLMs)-as-a-Service; and industry-specific AI agents-as-a-Service. "For the first time, enterprises in regulated industries do not have to choose between moving fast on AI and maintaining the governance and control their business requires. Rackspace is taking on that accountability," said Gajen Kandiah, CEO of Rackspace Technology. "We are not just providing infrastructure, we are committing to outcomes, and that changes the nature of the relationship between a technology partner and an enterprise customer." For enterprises, the conversation has shifted from choosing between GPUs and CPUs, or open versus proprietary Large Language Models (LLMs), to driving measurable business outcomes. Through this partnership, Rackspace brings deep expertise operating private clouds and optimizing public environments, backed by forward-deployed engineers. These forward-deployed engineers, embedded directly in customer environments, are trained on the Uniphore platform and accountable for delivering measurable outcomes from day one. The architecture is designed to extend across hybrid and public cloud environments, ensuring that enterprises with mixed deployment models can access the same governed, outcomes-based AI stack without compromise. The result is an end-to-end governed operating model delivering an outcomes-based service with control, accountability and measurable results. "Business AI Cloud adoption is seeing exponential growth globally due to its sovereign and open architecture and what enterprises are telling us is that they need business outcomes. Rackspace's adoption of Uniphore's Business AI Cloud allows our customers to access all five layers of our offering spanning Inferencing, Data, Knowledge, Model, and Agents, on Rackspace's secure and governed enterprise AI private cloud. For customers looking for sovereign AI solutions, this is a game changer," said Umesh Sachdev, CEO and Co-founder of Uniphore. Rackspace has over 20,000 mid-market and enterprise customers spanning industries such as healthcare, financial services, insurance and others. Additionally, Uniphore will move select enterprise inferencing workloads to Rackspace's private cloud to deliver a Sovereign AI offering. "This Rackspace-Uniphore deal packs real punch for organizations struggling to get beyond AI pilot mode", said David Cushman, Executive Research Leader, HFS Research. "It couples Uniphore's 'get-you-going-fast' AI platform with a Forward-Deployed Engineer (FDE)-style delivery model and governed private cloud, that gets you to value fast, but with control. That's something most mid-market organizations simply don't have and will be particularly welcome in regulated industries." "While the drive to scale AI across enterprise systems is high, many organizations find their progress stalled by the dual challenges of technical complexity and regulatory compliance," said Brian Jones, Chief Information Officer of Valley Medical Center. "It is a significant milestone to see Rackspace and Uniphore join forces. This partnership can be a game changer for organizations in highly regulated sectors, providing a robust framework to scale AI and extract tangible value from complex data landscapes." For customers, this partnership will deliver: * From AI pilot to production at scale: Most enterprises are stuck running AI experiments that never make it to production. This partnership delivers a unified, governed environment spanning infrastructure all the way to agents, enabling the path from pilot to production to be measured in weeks, rather than years. * Data that is ready for AI: Most enterprises have data, but not in a form that AI can use. Uniphore's data agents accelerate modernization, so enterprise data is structured, clean, and ready to power real workflows, without a multi-year transformation program standing in the way. * Industry-specific AI that teams can easily deploy: Pre-packaged solutions built on SLMs and agentic workflows provide business teams with a faster path to automation, with governance built in from the start. * The right compute at the right cost: Enterprises no longer have to over-provision or lock into a single architecture. Rackspace optimizes CPU and GPU environments, enabling each workload to run efficiently.

Rackspace and Uniphore partner to deliver "Infrastructure-to-Agents" Architecture for enterprise AI. Published March 10, 2026 Updated March 11, 2026 Enterprises have spent the past several years experimenting with artificial intelligence, yet many initiatives remain stuck in pilot phases. A new partnership between Rackspace Technology and Uniphore aims to address that gap by introducing what the companies call an "Infrastructure-to-Agents" architecture, a full-stack approach designed to help organizations move AI systems from experimentation into real-world production environments. Announced in early March, the collaboration combines Rackspace's hybrid multicloud and private cloud infrastructure with Uniphore's enterprise AI platform. The companies say the goal is to create an integrated environment where enterprises can deploy AI models, prepare data, and run autonomous AI agents while maintaining governance, security, and regulatory compliance. The effort reflects a broader shift in enterprise AI. Organizations are moving beyond questions about which models or chips to use and are focusing instead on how to translate AI capabilities into reliable business outcomes. The challenge of moving AI into production. Generative AI tools have spread rapidly across organizations, but building reliable systems that run in production remains difficult. Many companies face fragmentation across the AI stack. Infrastructure may be managed in one place, data pipelines in another, and AI models in yet another environment. The partnership seeks to address this fragmentation by combining two complementary layers. Rackspace contributes private cloud infrastructure designed to run AI workloads securely across CPU and GPU environments. Uniphore contributes its Business AI Cloud platform, which integrates models, data pipelines, knowledge layers, and agent-based automation. Together, the companies aim to provide a unified environment that covers the full lifecycle of enterprise AI. This includes preparing data, running inference workloads, managing models, and deploying AI agents that automate business workflows. Understanding the "Infrastructure-to-Agents" Stack. The concept of Infrastructure-to-Agents refers to treating the entire AI stack as a connected system rather than a collection of independent tools. Within this architecture, infrastructure supports the compute layer, data preparation pipelines transform enterprise data into usable inputs, models perform reasoning and prediction, and AI agents automate tasks within operational workflows. Under the partnership, enterprises will have access to inference environments capable of running on both NVIDIA and AMD compute architectures. The platform also provides data preparation services designed to structure enterprise data so it can be used effectively by AI models. Fine-tuned Small Language Models are another important component, allowing companies to deploy specialized models tailored to specific business functions. These models can then power AI agents that automate tasks across industries such as healthcare, finance, and insurance. Small Language Models play a particularly important in enterprise environments. Compared with large general-purpose models, they can be optimized for narrower use cases, operate more efficiently, and provide greater control over performance and governance. Uniphore's vision of the agentic enterprise. Uniphore's platform is built around the idea of the agentic enterprise, where AI agents perform structured work across business processes rather than simply responding to prompts. The company's Business AI Cloud platform combines several layers that work together. These layers include the infrastructure required for inference, the data and knowledge systems that organize enterprise information, the models themselves, and the agents that execute tasks based on those models. This architecture is designed to bridge the gap between consumer-style AI tools and enterprise systems that must meet strict requirements for reliability, security, and compliance. By integrating with Rackspace's infrastructure environment, the platform can operate inside private cloud deployments that are controlled by the enterprise. This approach allows organizations to deploy AI while maintaining control over sensitive data. Rackspace's in operationalizing AI. Rackspace contributes experience in managing complex cloud environments across both public and private infrastructure. Through the partnership, Rackspace engineers will workectly with enterprise teams to deploy and operate the combined platform. These engineers help configure infrastructure, optimize workloads, and ensure AI systems run reliably in production environments. This operational model reflects Rackspace's broader strategy of providing managed infrastructure services rather than simply delivering hardware or software components. The companies describe the offering as outcome-based, meaning the focus is on delivering measurable results rather than just deploying technology. Sovereign AI and regulated industries. One of the key drivers behind the collaboration is the growing demand for sovereign AI infrastructure. Industries such as financial services, healthcare, and insurance operate under strict regulatory frameworks. These organizations often require strong guarantees around data governance, privacy, and operational control. By running AI workloads inside private cloud environments and allowing enterprises to select the most appropriate compute architecture, the Rackspace and Uniphore platform is designed to meet these requirements. This approach allows organizations to adopt AI technologies while maintaining the security and compliance standards expected in regulated sectors. A shift toward operational AI. The partnership reflects a broader change in how enterprises are approaching artificial intelligence. In the early stages of the generative AI boom, conversations focused heavily on models and hardware. Organizations debated which large language models to adopt or which compute platforms offered the best performance. Today the focus has shifted toward operational integration. Enterprises are asking how AI can be embedded into real workflows, how systems can be governed safely, and how deployments can scale without creating new layers of complexity. By presenting a unified Infrastructure-to-Agents architecture, Rackspace and Uniphore are attempting to address these challenges at the system level. From experimentation to measurable outcomes. Ultimately, the goal of the partnership is to shorten the path from AI experimentation to production deployment. Many organizations still struggle with pilot projects that never scale beyond limited testing environments. A unified platform that integrates infrastructure, data preparation, models, and AI agents could help reduce those barriers. If successful, the collaboration may illustrate an emerging pattern in enterprise AI: the next phase of adoption will depend less on new models and more on the ability to integrate AI systems into secure, governed, and operational technology environments. Antoine is a visionary leader and founding partner of Unite.AI, driven by an unwavering passion for shaping and promoting the future of AI and robotics. A serial entrepreneur, he believes that AI will be as disruptive to society as electricity, and is often caught raving about the potential of disruptive technologies and AGI. As a futurist, he is dedicated to exploring how these innovations will shape our world. In addition, he is the founder of Securities.io, a platform focused on investing in cutting-edge technologies that are redefining the future and reshaping entire sectors.