Data-Centric AI

Modeling is rarely the hardest part anymore. More often the bottleneck is framing the right problem and finding the data to answer it. Data-centric AI starts from a simple observation: progress happens one dataset at a time. This matters more in chemistry than in images or text, because experimental data is scarce, expensive and scattered across labs. A large part of our work is curating, generating and releasing datasets and benchmarks that let the field measure real progress.