1
min read
Challenge
Caterpillar has worked with customers on autonomous mining for 15+ years, but construction is a different problem: unstructured, constantly changing, and spread across residential, commercial, infrastructure, and highway work. Teaching models to operate there takes huge volumes of synchronized machine data plus simulation for scenarios field collection can’t cover, which demands serious GPU capacity and expertise.
Solution
Caterpillar began working with CoreWeave in 2026, choosing it for the GPU capacity to train on very large data volumes and a team that knows how to apply physical AI. With NVIDIA, it uses AI to annotate machine data and runs the full loop around training, from data prep to simulation to evals, at scale.
Benefits
- Experts back on expert work: AI-assisted labeling replaces teams reviewing recordings, so specialists improve models instead of tagging data.
- Capacity for synthetic data: For every hour of real data, Caterpillar expects roughly 1,000 simulated hours.
- Built for the whole loop: Data prep, training, simulation, and evals run together, not just GPU access.
- Faster cycles: Development that once took years is now measured in months.
