You have an infrastructure problem, not an AI problem

CDFAM Computational Design Symposium — Barcelona 2026

Javier Blanco Cordero · Quix

Engineering teams have found AI useful for discrete tasks: SQL queries, graphs, summaries. Higher-value work, root cause analysis, physics-based ML, production deployment, remains out of reach. The standard diagnosis is that the models need to improve. Javier Blanco Cordero's experience points the other way: the models are already sufficient. The infrastructure surrounding them is not.

Using a real rotor balancing case involving 50,000 rotors per year, this talk walks through what it takes to move AI from task-level support to autonomous engineering investigation. With the right infrastructure in place, a model that had underperformed on summary data identified a discriminating signal in 100 kHz vibration data that decades of domain expertise had missed. That same model independently designed a deployment plan that reduced total balancing runs by 41%.

The presentation covers what was built, the results, and what it means for any organization sitting on complex engineering data.

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