Magic Research has introduced a private, white-label generative AI platform that operates entirely within an organization’s own infrastructure.
The company says the system can cut AI costs by up to 90 percent while keeping sensitive data secure. It gives enterprises full control over their branding, data, and operations without relying on vendor-owned AI services.
The platform, Magic Private AI, is aimed at sectors where privacy and compliance are critical. Unlike cloud-based chatbots, like ChatGPT, it runs behind an organization’s firewall on its own hardware and under its own name.
This is designed to address the concerns of enterprise leaders who see data privacy as a top issue when adopting AI, as well as the integration challenges that come with legacy systems and cloud services.
Magic Private AI offers a full range of capabilities including intelligent data retrieval, drafting, advanced research, complex analysis, retrieval-augmented generation, and agentic systems.
It can be deployed on cloud providers, private GPUs, data centers, or even existing laptops and workstations, without relying on external APIs or investing in new infrastructure.
AI capabilities at scale
The company says the platform is designed for regulated and high-stakes environments such as technology, finance, healthcare, legal, and government. It can help organizations deploy secure, branded AI capabilities at scale without compromising privacy, budget predictability, or performance.
Compliance features are built into Magic Private AI through GatewAI. This tool enforces policies, filters content, logs activity, and aligns with regulations including FERPA, HIPAA, GDPR, and SOC 2. The company says this ensures that all prompts, documents, and logs remain within the organization’s own systems.
Humberto Farias, founder of Magic Research, said, “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.”
Magic Private AI is powered by the company’s Fabric Hypergrid, described as the industry’s first private, generative AI distributed computing hypergrid.
The Orlando, Florida-based firm says this allows organizations to turn their existing hardware into an enterprise-grade AI supercomputer at a fraction of the cost without compromising speed, security, or scalability.
What do you think about organizations running their own private AI instead of relying on public cloud services? Let us know in the comments.
