GC Rieber VivoMega & Veas

Industrial teams have never had more process data, but turning that data into faster decisions and better operations is still hard. Veas and GC Rieber VivoMega are now using AI assistants connected to Intelecy to investigate deviations, uncover recommendations, create machine learning (ML) models, and move faster from insight to action.

Connecting AI assistants to real process context

The connection is enabled by MCP, or Model Context Protocol: an open standard that allows AI assistants to securely connect to external tools and data sources. In an industrial setting, that matters because generative AI assistants are only useful if they can work with the plant’s real context: live sensor data, historical trends, asset structures, AI models, and process knowledge.

When Intelecy launched support for Claude, ChatGPT, and Microsoft Copilot through MCP, the idea was simple: let engineers work with live plant data through the tools they already use, without losing the industrial context that makes the answers trustworthy. The AI assistant becomes the interface, while Intelecy provides the industrial intelligence behind it. Users can investigate anomalies, explore process relationships, create dashboards, and even create new ML models. The models are then trained, managed, and operationalized in Intelecy, turning a conversation about plant performance into a practical path toward process optimization.

From dashboards to dialogue

Veas and GC Rieber VivoMega are among the early users of this new way of working with Intelecy. Both companies already use Intelecy to turn industrial data into measurable operational gains: lower chemical and energy use, higher uptime, better yield. A few months in, they are describing something bigger than a productivity gain: more of the organization able to act on what the plant is telling them. By connecting Claude to Intelecy through MCP, they can bring process data, operational context, and Intelecy’s AI-driven insights into a conversational interface, making it easier to move from questions to model-driven insights such as anomalies, predictions, and recommendations, and from there to shared action.

Instead of running repeated manual analyses to see what patterns emerge, users can discuss the issue directly with Claude, which has access to the relevant Intelecy context and industrial AI insights.

For Veas, the promise is not simply faster analysis. It is the ability to make process insight more accessible across the organization. When engineers and domain experts can ask questions directly of operational data, more people can contribute to understanding how the plant behaves and where improvements can be made. 

For Torbjørn Saltkjelvik,  R&D Process Optimizer at GC Rieber VivoMega and one of the plant's earliest Intelecy users since 2019, the change shows up most clearly in how much time it takes to get from a question to an answer.

The pattern across both stories

 Veas and GC Rieber VivoMega operate in different industries and work toward different goals, but they describe the same shift: less time spent running analyses to see what turns up, and more time spent asking direct questions of the plant and getting answers grounded in real process data. When users can go from asking a question to getting actionable recommendations and creating a model that is trained and managed in Intelecy, the AI assistant becomes a starting point for faster anomaly detection, better decision support, and continuous process optimization. 

• Intelecy MCP launch announcement
  
• Explore the Intelecy Industrial MCP Server
  
• Industrial Use Cases
• Customer Cases
  

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