The MCP protocol: why it's important and how to use it in an industrial setting. Published on 02/11/2026 Summarize this article with: Frequently asked questions. Is the MCP protocol a standard? Yes. MCP (Model Context Protocol) is an open standard, developed by Anthropic in 2024 and rapidly adopted by various AI platforms and tools. Its governance has evolved over time, and the protocol is now part of the Agentic AI Foundation's ecosystem. Does the MCP protocol have any security-related functions? MCP incorporates security mechanisms and requirements, including an authorisation model for HTTP connections. However, authorisation is not mandatory for all implementations, and the protocol alone does not guarantee the security of the entire application. In an industrial context, it is therefore essential to assess how the server integrates with the security mechanisms of products and infrastructure. This is why AVEVA is taking charge of the development of its own MCP Servers. Can I send instructions to another piece of software via an MCP Server? If a piece of software can receive instructions from an external source, for example via dedicated APIs, these can be exposed via an MCP Server. It is up to the person implementing the server to decide which functions to make available and at what level of security. Does AVEVA supply MCP Servers for its solutions? MCP technology is at the heart of AVEVA's development strategies. The MCP Server for Historian is due to be released in early 2027, followed by releases for other products such as PI Server, MES and System Platform. AVEVA's MCP servers will be natively integrated with the products' security mechanisms, with the aim of ensuring implementations that are secure by design. Read more news. Contact Factory Software. Please feel free to contact Factory Software to learn more about the solutions Factory Software offer or to request a product demonstration - either remotely or at one of its showrooms.

Digital twin simulation for megawatt AI data centers: an innovative AVEVA solution! October 23, 2026. AVEVA boosts efficiency in hyperscale data centers. AVEVA offers an integrated digital twin infrastructure that optimizes power, cooling, and workload efficiency in large-scale computing facilities using virtual environments. This innovative approach is realized through an end-to-end digital twin simulation project, developed in collaboration with Schneider Electric, Nscale, and Start Campus, to model and optimize the physical infrastructure operations of AI data centers. Powerful integration with NVIDIA Omniverse DSX Blueprint. Built on the NVIDIA Omniverse DSX Blueprint, the Immersive Twin platform accelerates deployment in high-density data center ecosystems by combining power, cooling, and electrical models with AI workload requirements. Application at Portugal's SINES Data Campus. This simulation technology is being applied to operations at Nscale's SIN01 data center within the 1.2-gigawatt SINES Data Campus in Portugal. Developed by Start Campus, the facility is designed to support high-capacity AI processing and will house over 66,000 NVIDIA Rubin Graphics Processing Units (GPUs). By creating a unified digital model of such a large deployment, operators can assess the thermal and electrical impacts of high-density computing clusters before physical hardware installations are performed. Operational data integration and simulation mechanics. The digital twin infrastructure leverages the OpenUSD framework to cross-platform connect asset data from AVEVA, ETAP, Schneider Electric, and third-party vendors. The system ingests engineering and operational data to create simulations covering process behavior, electrical distribution, and liquid cooling loops. This capability allows facilities to run precise scenarios for hardware configurations, for example, modeling how an increase in rack power density affects the broader power chain, thereby reducing supply risks and minimizing physical infrastructure changes. Workload efficiency and lifecycle management. For AI cloud platforms like Nscale, aligning physical infrastructure performance with computing requirements directly impacts token generation efficiency. AVEVA's software suite, including CONNECT, PI System, Unified Engineering, and Asset Information Management, provides continuous visibility into asset performance. By visualizing these metrics through NVIDIA Omniverse applications, operators manage the entire lifecycle of an AI factory, transitioning from initial facility design to ongoing operational optimization based on active thermal and electrical loads. AVEVA's edge in a competitive landscape. In the data center infrastructure management (DCIM) and physics-based industrial simulation sectors, AVEVA's software portfolio competes with solutions like Siemens Xcelerator and Bentley Systems' iTwin framework. However, a key competitive differentiator of the AVEVA software ecosystem, working in conjunction with Schneider Electric hardware, is its native integration with the NVIDIA Omniverse DSX Blueprint. This architecture provides GPU-accelerated 3D visualization and simulation based on the OpenUSD standard, enabling simultaneous modeling of both physical cooling loops and specific IT workload outputs, such as AI token processing efficiency. While traditional DCIM platforms often monitor this data as separate silos, AVEVA's integrated approach offers a comprehensive view.

Schneider Electric, AVEVA, Nscale and Start Campus pilot AI digital twin. Key takeaways. * Schneider Electric, AVEVA, Nscale and Start Campus have launched one of the first end-to-end AI-driven digital twins built on NVIDIA Omniverse DSX Blueprint. * The pilot runs in the 1.2 GW SIN01 data-center at Portugal's SINES Data Campus, soon to host >66,000 NVIDIA Rubin GPUs. * By merging real-time power, cooling and workload data, the Immersive Twin delivers a single, predictive view that can cut energy use by up to 15 % and speed up token-production cycles by 30 %. * The project validates a reusable blueprint for gigawatt-scale AI factories, paving the way for faster design-build-operate-maintain loops across Europe. Schneider Electric, AVEVA, Nscale & Start Campus unveil ai-powered digital twin pilot. Background. A consortium of four industry leaders - Schneider Electric (global energy-tech provider), AVEVA (industrial-software specialist), Nscale (full-stack AI cloud platform) and Start Campus (data-center developer/operator) - has announced a live demonstration of an AI-centric digital twin. The solution is constructed on the NVIDIA Omniverse DSX Blueprint, part of NVIDIA's DSX Sim suite for high-fidelity simulation and AI-ready infrastructure. What the Immersive Twin does. | Feature | Traditional Approach | Immersive Twin (DSX Blueprint) | | Data Integration | Separate SCADA, BMS, and workload logs; manual correlation | Unified model ingesting power, cooling, network and AI-workload telemetry in real time | | Simulation Fidelity | 2-D schematics, limited physics | Full 3-D physics-based simulation with GPU-accelerated thermal and electrical analysis | | Decision Latency | Hours-to-days for scenario testing | Sub-second "what-if" queries powered by NVIDIA RTX-A6000 GPUs | | Scalability | Up to a few MW per model | Designed for gigawatt-scale AI factories (1.2 GW campus) | | AI Integration | Post-processing analytics | Embedded generative AI for predictive maintenance and load-balancing | The Immersive Twin fuses Schneider Electric's EcoStruxure(TM) digital-twin framework with AVEVA's engineering suite (including the Unified Engineering and System Platform). Nscale supplies the AI workload orchestration, while Start Campus provides the physical plant - the SIN01 data-center on the 1.2 GW SINES Data Campus. Pilot scope. * Location: SIN01, part of the 1.2 GW SINES Data Campus, Portugal. * Hardware Footprint: Planned deployment of >66,000 NVIDIA Rubin GPUs (each delivering up to 130 TFLOPS FP16 performance). * Energy Target: Reduce overall PUE (Power Usage Effectiveness) from the current 1.35 to <=1.20 within 12 months. * Token Production: Accelerate Nscale's AI-token generation pipeline by 30 %, cutting average token-creation time from 45 min to ~31 min. The twin continuously ingests sensor streams (temperature, humidity, voltage, current) and AI workload metrics (GPU utilisation, memory bandwidth). It then runs GPU-accelerated thermal-fluid simulations to predict hot-spot formation and recommends real-time cooling set-point adjustments. Why it matters. * Energy Efficiency at Scale - With AI workloads projected to consume 30 % of global electricity by 2030, a 15 % reduction in data-center energy use translates to ≈150 MW saved across a typical gigawatt-scale campus. * Speed to Market - The ability to test hardware-software configurations virtually eliminates weeks of physical prototyping, enabling faster rollout of new AI services. * Reusability - The DSX Blueprint is packaged as a validated, exportable model, allowing other operators to replicate the workflow with minimal custom development. Future outlook. The consortium plans to extend the pilot to two additional sites in 2027, each targeting a minimum of 50 % higher GPU density. A roadmap includes integration with NVIDIA's DGX Cloud for seamless workload migration and the addition of generative AI assistants that can auto-generate optimisation scripts based on twin insights. Bottom line. The Schneider Electric-AVEVA-Nscale-Start Campus partnership demonstrates that a high-fidelity, AI-ready digital twin built on NVIDIA's Omniverse DSX Blueprint can deliver measurable energy savings, faster AI-token production, and a repeatable blueprint for gigawatt-scale AI factories. As AI workloads continue to dominate power consumption, such immersive twins will become essential tools for operators seeking to balance performance, sustainability and cost.