Elastic compute for simulation and AI inspection

• Simulation and model training need peak capacity, but building a cluster takes months and sits idle the rest of the year.
• AI inspection and equipment maintenance need several models working together; a single model cannot solve the business problem.
• Process parameters and R&D data are core assets, so moving them to a public cloud is a hard sell.

1. 01

   Elastic capacity

   Rent GPU hours for the project cycle. Scale up for simulation and training peaks, release at the end, and never pay for idle hardware.

2. 02

   Multi-model orchestration

   A vision model handles "seeing" and a knowledge-graph model handles "fixing", orchestrated through one API into a closed business loop.

3. 03

   Clear data boundaries

   Sensitive data can stay inside your domain, with only de-identified features used for inference.

Delivered: A leading auto parts supplier: 60% faster simulation and 35% lower annual compute cost.