Bridging the Gap: The Challenge of Providing AI Models with the 3D Data

Concrete AI use cases are now emerging where companies train their own models tailored to domain-specific engineering challenges. Mechanical engineering teams are moving beyond pure research into early applications, with growing opportunities to leverage 3D CAD data for specialized training. To make these models effective, however, reliable access to high-quality 3D data is crucial — the better the training data, the better the model. At the same time, 3D data remains difficult to access on a larger scale.

Introducing "Context-Selective 3D Data Access for AI Model Training”

We’re exploring an approach we call Context-Selective 3D Data Access for AI Model Training.

With instant3Dhub, users can curate and provide only the most relevant 3D data elements for each use case, rather than entire data sets. This targeted selection enables more efficient and effective AI training.

Instead of supplying full CAD assemblies—like handing over an entire textbook when only one chapter is needed—we extract and deliver only what’s required, such as:

• Product structure

• Geometry definitions

• Metadata

• PMI and model views

• Other context-relevant 3D data entities

This isn’t about reducing data quality; it’s about intentional, context-aware training that drives better results.

Purpose-Built Infrastructure: Making 3D Data Accessible for AI

instant3Dhub is designed to support domain-specific AI training at scale by connecting directly to existing data backends, such as PLM systems, and preparing 3D data for real-time, on-demand streaming through a unique caching mechanism.

All relevant data - structure, geometries, metadata, etc. - is accessible via 3D Spaces: digital work rooms where information is coherently structured and accessible.

Developers and AI engineers interact with these 3D Spaces via APIs, enabling:

• Intelligent curation: Extract only the contextually relevant 3D data for each training scenario.

• Precise access: Deliver targeted data slices—product structure, geometry, metadata, PMI, and model views—tailored to the training task.

This architecture ensures that training data remains purposeful, scalable, and preserves essential engineering context.

instant3Dhub as enabler between AI Agents and 3D

For targeted training scenarios, this selective streaming architecture shows early promise:

• Less noise in AI training data leading to better model results

• Stronger traceability from source data to AI model outcomes leading to

• Tighter feedback loops between engineering expertise and model learning leading to faster results and higher quality

Looking ahead, instant3Dhub is set to play a key role connecting AI and 3D engineering data. With structured, real-time access through APIs, instant3Dhub can become the essential infrastructure that makes 3D data truly usable for AI. This idea will be explored more in future articles, but it already guides how we make data accessible and ready for AI agents.

This is Just the Beginning

We are early in our journey. Some features, like on-demand extraction in an AI Training scenario, are possible but still experimental. Moving these into scalable product offerings will need more validation and collaboration. Early use cases however show strong potential for AI integration in engineering.

We are also exploring deep integration with knowledge graphs to improve connections between data entities and unlock richer context for 3D engineering data.

If you’re facing similar challenges with making CAD data usable for AI, write me via LinkedIn or our usual channels.