TLDR: Magic Research has launched a groundbreaking white-label generative AI platform designed to operate entirely within an organization’s own infrastructure. This innovative solution promises to drastically cut AI costs by up to 90% while ensuring unparalleled data security and full data sovereignty. It addresses critical enterprise concerns regarding privacy, compliance, and vendor lock-in, offering a customizable and scalable AI environment for sensitive sectors.
Orlando, FL – August 14, 2025 – Magic Research, a pioneering entity in artificial intelligence and advanced computational intelligence, today announced the debut of its new white-label, enterprise-ready generative AI platform. This solution marks a significant shift in AI deployment, allowing businesses to run advanced AI capabilities entirely in-house, thereby gaining complete control over their data, infrastructure, and brand identity.
The core appeal of Magic Research’s offering lies in its ability to deliver substantial cost reductions, with the company claiming potential savings of up to 90% on AI expenditures. Crucially, the platform prioritizes data security by ensuring all sensitive information remains within the organization’s private infrastructure, whether on-premises or in a private cloud. This ‘full data sovereignty’ means that all prompts, documents, and logs never leave the company’s control, directly addressing a top concern for enterprise leaders adopting AI.
Designed specifically for regulated and high-stakes environments, including technology, finance, healthcare, legal, and government sectors, Magic Private AI is built with robust compliance features. Its integrated tool, GatewAI, enforces policies, filters content, and logs activity, aligning with stringent regulations such as FERPA, HIPAA, GDPR, and SOC 2. This ensures that organizations can deploy secure, branded AI capabilities at scale without compromising privacy, budget predictability, or performance.
Humberto Farias, founder of Magic Research, articulated the company’s vision: “Many AI vendors are hosted on third-party clouds. We believe businesses will demand their own private intelligence — trained on their own data — that not only protects privacy but is also fully customizable to fit unique workflows and operational needs. Our Private AI makes that vision a reality: delivering a solution that is both secure and practical for everyday business use. By keeping AI capabilities in-house, organizations can innovate confidently, avoid costly vendor lock-ins, and maintain compliance without sacrificing performance or flexibility.”
The platform is powered by MAGIC’s Fabric Hypergrid, touted as the industry’s first private, generative AI distributed computing hypergrid. This innovative technology enables organizations to transform their existing hardware into an enterprise-grade AI supercomputer at a fraction of the cost, without sacrificing speed, security, or scalability. Deployment is highly flexible, supporting cloud providers, private GPUs, data centers, and even existing laptops or workstations, eliminating the need for external APIs or significant new infrastructure investments.
Magic Private AI offers a comprehensive suite of capabilities, including intelligent data retrieval, sophisticated drafting, advanced research, complex analysis, retrieval-augmented generation, and agentic systems. Its versatility was highlighted by Mrs. Priscilla Araújo, CEO at Pluris Academy, who stated, “At Pluris, we’re always looking for ways to integrate innovation while protecting our students’ privacy and values. MAGIC Private AI allows us to explore cutting-edge educational tools within our own secure infrastructure. It’s empowering to bring AI into the classroom on our terms—safely, responsibly, and in alignment with our mission.”
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This launch positions Magic Research as a key player in providing secure, customizable, and cost-effective AI solutions that empower enterprises to leverage the full potential of artificial intelligence while maintaining complete control over their most sensitive data.
