The adoption of Artificial Intelligence in the business environment has doubled over the past two years; however, in most organizations, its use remains largely confined to public GenAI tools. Many companies are reluctant to upload sensitive data to public services such as ChatGPT, Gemini, or Copilot, which is driving growing interest in Private AI—a model focused on data sovereignty, security, and regulatory compliance. This shift is particularly relevant in the context of regulations such as the EU AI Act, which came into force across all EU Member States, including Romania, in August 2024.

Private AI enables full control over sensitive data, ensures compliance with regulatory requirements, and significantly reduces security risks—key considerations for nearly 40% of organizations adopting AI, according to McKinsey, The State of AI in 2025. Deploying AI applications on proprietary IT infrastructure allows organizations to implement advanced cyber-resilience mechanisms, such as air-gapped or immutable backups, and to maintain end-to-end control over the entire data lifecycle, from model training to inference. At the same time, on-premises AI delivers high performance and low latency for mission-critical use cases—such as fraud detection or real-time medical diagnostics—while providing predictable and sustainable long-term costs.

 

 

For organizations pursuing these objectives, Datanet Systems has developed an end-to-end, enterprise AI solution that unifies infrastructure, orchestration, and AI capabilities within a secure, scalable, and easy-to-operate framework. Leveraging Datanet Systems’ deep expertise in data centers, high-performance networking, cybersecurity, and cloud-native technologies, the solution integrates seamlessly with existing IT environments, enabling organizations to adopt AI in a controlled, efficient, and accelerated way.

 

AI interest is growing. How prepared are organizations to turn investment into tangible results?

According to the Cisco 2025 AI Readiness Index, 53% of companies plan to develop complex AI applications over the next 12 months; however, only one in three has an IT infrastructure that is sufficiently flexible and scalable to meet these demands. As a result, many AI initiatives remain stuck in pilot phases or progress far more slowly than anticipated, due to challenges related to infrastructure limitations, data governance, security, scalability, and integration with existing systems. At the same time, pressure to deliver measurable outcomes is increasing: eight out of ten organizations are required to demonstrate tangible ROI, yet only one-third have clearly defined processes in place to measure the impact of their AI initiatives.

 

What does an IT environment ready for private AI look like?

 

• Scalable compute, storage, and networking capabilities, sized for data-intensive AI workloads, including parallel processing and high-throughput data transfers.
• Specialized hardware—such as GPUs, TPUs, or other AI accelerators—optimized for training and inference workloads, with support for virtualization, partitioning, and efficient resource utilization.
• Native support for advanced workloads, with compatibility and optimizations for natural language processing (NLP), computer vision, deep learning, and high-performance computing (HPC).
• Flexible, modular infrastructure, featuring configurable hardware and software stacks capable of supporting use cases such as predictive analytics, fraud detection, process automation, or real-time inference, while maximizing performance and operational efficiency.
• Built-in resilience and security, including data protection mechanisms (immutable and air-gapped backups), access controls, network segmentation, and continuous monitoring of AI workloads.

Organizations that succeed in deploying a mature AI infrastructure— encompassing data governance, MLOps, and operational capabilities—achieve measurable benefits, including optimized costs, reduced latency, increased productivity, and faster time-to-value for AI use cases. The impact is reflected directly in the ability to scale mission-critical applications and improve operational reliability. Conversely, the absence of a robust technological foundation limits AI adoption and scalability, leading to efficiency losses and missed opportunities, as highlighted by the Cisco 2025 AI Readiness Index.

In this context, an end-to-end approach that integrates infrastructure, security, orchestration, and AI operations into a unified framework becomes essential to transforming AI into a predictable, secure, and scalable IT service.

 

Datanet AI end-to-end solution: unified infrastructure, orchestration, and AI capabilities

 

Datanet Systems’ solution is built on a modular and scalable architecture optimized for complex enterprise AI workloads. It covers the entire technology stack required for AI implementation, organized across three layers that combine hardware, orchestration, AI/ML pipelines, and application components into a single, secure, high-performance ecosystem, designed for stable and predictable operation.

For network infrastructure, the solution leverages Cisco Nexus, while container orchestration and AI/ML automation are powered by Red Hat OpenShift AI, with Datanet Systems maintaining strategic partnerships with both vendors.

Networking and computing

The network infrastructure includes high-performance Ethernet switches and the Cisco Nexus platform with 100G/400G/800G speeds, deployed in Spine-Leaf topologies without oversubscription. AI-specific protocols such as RoCEv2 ensure low-latency, high-throughput data transfer.
Compute resources for AI workloads are provided by enterprise servers equipped with GPUs, either directly in compute servers or in dedicated AI acceleration systems, configured with 2, 4, or 8 GPUs. Architectures such as NVIDIA HGX and MGX allow performance scaling according to workload complexity, providing flexibility and modularity.

Middleware for automation and orchestration

The solution leverages Red Hat OpenShift AI, an extension of the OpenShift Container Platform, enabling container orchestration and migration of traditional workloads to modern AI/ML stacks. It also automates application development, deployment, and management, ensuring scalability, consistency, and performance.
OpenShift AI provides a complete set of services across the AI/ML lifecycle: data collection, storage, and preparation; model development with ML notebooks and standard libraries; CI/CD integration and lifecycle management; and performance monitoring and model governance. This transforms an organization’s existing OpenShift infrastructure into a comprehensive AI platform, enhancing security without disrupting existing workflows.
In addition, OpenShift AI integrates vLLM, an optimized inference engine capable of running any large language model (LLM) such as Llama, Mistral, Gemma, or Qwen. vLLM delivers up to 24x higher throughput than standard frameworks and supports dynamic GPU memory management.

AI Applications 

Datanet Systems develops custom AI applications that leverage LLM technologies to support a broad range of business processes. These models can be trained on each client’s proprietary data—structured, semi-structured, or unstructured—including databases, documents, OCR-processed content, and audio-video files converted through speech-to-text technologies.

Key application areas include:

• Automated text analysis (NLP) – rapidly process large volumes of text to extract relevant insights, relationships, and events.
• Proactive alerts and risk scenarios – anticipate behavioral patterns and automatically flag potential high-risk situations.
• AI assistants for human analysts, based on retrieval-augmented generation (RAG) architectures, providing operational “copilot” support for natural language queries, data insights, reports, and analysis.
• Multilingual and cross-cultural analysis – leveraging multilingual LLMs (e.g., LLaMA, mBERT, Mistral) to analyze text across languages, deliver contextual translations, and detect cultural or linguistic ambiguities.

All AI applications are customized to each client’s requirements and can be deployed on on-premises infrastructure, ensuring full data control, advanced security, compliance, and predictable performance.

If your organization has ambitious plans for AI but does not want to rely solely on public GenAI tools, the solution is to train and run your own LLMs on internal datasets within a controlled, secure IT environment. Datanet Systems provides everything needed—compute, storage, networking, and applications—to transform your existing infrastructure into an enterprise-ready AI platform. For technical details or demo sessions, contact sales@datanets.ro.