Elon Musk recently stated that the future of the job market belongs to AI and robots, which will take over all jobs by 2030. While this is clearly an exaggeration, the trend can no longer be ignored, and the change is already underway: artificial intelligence has become an inevitable driver of business transformation, reaching record levels of adoption. However, the real question is no longer just how much we use AI, but how we use it.
While public AI opened the door to innovation through ChatGPT (OpenAI), Google Gemini, or Microsoft Copilot, the next essential step is Private AI—a model that emphasizes control, security, and compliance, especially in the context of regulations such as the EU AI Act.
Private AI therefore, marks a new stage: if data is the most valuable asset, intelligent protection of that data becomes the top priority. With M247 Global, organizations can take advantage of enterprise-grade AI servers, preconfigured data center infrastructure, and modern orchestration frameworks such as Retrieval-Augmented Generation (RAG), Ollama, and the Model Context Protocol (MCP). This enables companies to build, deploy, and run large language models (LLMs) within private, secure, and scalable environments, tailored to their operational and compliance requirements.
If you’re attending Capacity Europe 2025 or MSP Barcelona 2025 this October, you’ll have the opportunity to discuss these solutions directly with M247 Global representatives on site.
In the meantime, you can discover how they work in practice in our dedicated use case:
AI Infrastructure – M247 Global
The Limits of Public AI
Public AI models are trained on public datasets and continue to learn from the information users input daily. While this may seem like an opportunity, in reality, it opens a Pandora’s box: sensitive data can be collected without consent, reused without permission, or stored in ways completely outside an organization’s control. This creates the risk that confidential information could be used to train models, shared with third parties, or even exposed through leaks and cyberattacks. Moreover, the lack of transparency regarding data sources and usage generates compliance issues and legal vulnerabilities. Although Public AI promises a rapid boost in efficiency and innovation, it can also carry a costly downside: losing control over a company’s most valuable data.
Private AI: The Answer to Public AI’s Limitations
Private AI overcomes these limitations. As the name suggests, it refers to AI infrastructures developed and operated by organizations for internal use. Private AI means using AI models developed and run in a controlled environment to protect sensitive data. Unlike Public AI, which centralizes information in the provider’s cloud, Private AI keeps data under the organization’s control—either on-premises or in a secure private cloud, encrypted or anonymized. This approach allows companies to train models, including LLMs, using their own data, reducing risks of exposure, leaks, or compliance breaches. Private AI therefore, embodies a “security-first” strategy, combining the power of artificial intelligence with data protection and the confidentiality of internal information.
3 Essential Technologies for Using Private AI in Organizations: RAG, Ollama, MCP
- Retrieval-Augmented Generation (RAG)
RAG allows organizations to continue using public AI models without exposing internal data. The RAG system first queries the company’s internal databases, documents, and other resources to identify relevant information. This retrieved data is then integrated into the original request and sent to the public model, which generates the final response. The public model’s answers are thus enriched with internal information while protecting the confidentiality of the company’s data.
- Ollama
Ollama is a platform that enables downloading and running large language models (LLMs) directly on a local computer, without relying on external servers and without the data leaving the user’s secure environment. Essentially, the application runs in the background on Mac or Windows and provides both a command-line interface and an API, facilitating the integration of models into custom, internally developed applications. Ollama uses quantization techniques to reduce resource requirements, allowing LLMs to run efficiently even on standard laptops or desktops. This way, organizations and users can utilize AI offline, keeping data local and ensuring better control over the security and privacy of information.
- Model Context Protocol (MCP)
MCP defines how AI models can safely discover and use internal tools and data sources. Through MCP, a company’s applications, APIs, and systems become accessible to AI, which can identify available actions and execute them automatically without additional coding. This makes AI integration into workflows simple, secure, and flexible, while ensuring that data always remains within the organization’s controlled environment, maintaining confidentiality and full control over sensitive information.
The Benefits of Adopting Private AI
Private AI provides organizations with greater efficiency, control, and security through a series of key benefits:
- With Private AI, organizations maintain full control over the development, deployment, and integration of models into their IT infrastructure. Models trained on internal data deliver results tailored to the company’s needs, reduce the risk of exposing sensitive information, and can integrate effectively even with legacy systems.
- Public AI involves significant risks for companies, as data entered can be accessed, stored, and used by the provider. With Private AI, organizations retain control over policies, data, and access, keep sensitive information internal, reduce risks of leaks and errors, minimize dependence on external platforms, and facilitate audits and regulatory compliance.
- Running AI models in local environments or private clouds provides faster response times, improved application efficiency, and the ability to continuously optimize performance for the company’s specific needs without relying on provider plans.
- Private AI reduces exposure to cyber threats by minimizing risks of attacks, data breaches, and unauthorized access through operation in a secure infrastructure with strictly controlled and monitored access.
- Private AI models offer greater transparency, allowing companies to track how decisions are made and ensure compliance with ethical standards and current regulations.
- Private AI decreases dependence on external services, avoiding risks of vendor lock-in, service unavailability, or price fluctuations.
Transform Your Organization’s Infrastructure into a Private AI Hub with M247
M247 provides organizations with comprehensive solutions and services for deploying and running LLM models in a private, secure, and scalable environment. These include high-performance AI servers equipped with NVIDIA GPUs (up to 10 GPUs per node), optimized for ML libraries such as TensorFlow and PyTorch, as well as a custom architectural design based on technologies like RAG, Ollama, and MCP, tailored to each client’s workflows. Additionally, M247 offers end-to-end deployment and management services, from server and software configuration to continuous performance monitoring.
M247 also provides hosting and colocation services in Tier 3 data centers, ensuring redundancy, low-latency connectivity, and 24/7 support, along with specialized technical assistance for integration with internal applications and databases, infrastructure scaling, and compliance maintenance. This allows organizations to run LLMs in a controlled environment, achieving maximum performance, security, and flexibility.
Unlock the full potential of Private AI on your own infrastructure with M247. Explore how here.