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Many AI-for-engineering startups lean on synthetic data — procedurally generated geometries paired with automated simulation results. While this scales easily, it misses something critical: the reasoning behind engineering decisions.

Khorium’s foundation model is trained exclusively on real engineering data — actual CAD files, real simulation setups, and genuine design decisions made by experienced engineers. This creates a flywheel: every new customer project improves the model, which in turn delivers better results for all customers.

The difference shows up in edge cases. Synthetic-trained models handle textbook geometries well but struggle with the messy reality of production engineering. Real-data training captures the nuance that separates a good mesh from a great one.