Every infrastructure operator today wants to make data “AI-ready.” But what does that really mean? Most organizations still treat AI as something you plug on top of your data – as if algorithms can magically find structure in spreadsheets, CAD files, GIS layers, and sensor streams. The reality is: if your data isn’t organized around meaning, AI can’t use it. That’s where ontology comes in.

We’ve just deployed a new Nextspace Cloud region in South Korea, expanding our global infrastructure footprint.

For decades, plant and process teams have relied on two essential, but disconnected, views of their assets: - 2D schematics that capture system logic, flow, and connectivity, and - 3D models that represent the physical world.

Kyungnam University, RCK and Nextspace sign a global partnership to accelerate manufacturing innovation. This aims to offer small and medium sized manufacturers (SMMs) an AI powered opportunity to automate operations and compete more successfully globally.

In asset-intensive industries, AI rarely fails because models are weak – it fails because data lacks context.

Digital twins have come a long way. What began as a way to see assets has become a way to understand them. But most twins still stop short of true intelligence. They visualise geometry -- they don’t model knowledge.