Snorkel

Snorkel provides specialized data infrastructure for frontier AI labs and research teams that need to build, benchmark, and evaluate models operating in high-stakes, domain-specific environments. The platform focuses on creating custom training datasets, benchmarks, and evaluation environments that address the failure modes of cutting-edge models—particularly where standard pipelines fall short. Frontier models often break on distributional gaps, benchmark blind spots, and tasks with ambiguous correctness criteria; Snorkel directly tackles these problems by enabling users to generate targeted training data that capture edge cases, rare phenomena, and nuanced domain knowledge. The system supports the development of supervised fine-tuning datasets, preference alignment data, and structured evaluation suites for both large language models and agent-based systems. For example, teams can design benchmarks that simulate real-world deployment conditions in fields like healthcare, law, finance, or scientific research, where a single misclassification carries high consequences. Snorkel also facilitates the creation of continual learning benchmarks, such as the Continual Learning Bench developed in collaboration with Berkeley, which tests how models adapt to shifting distributions over time without catastrophic forgetting. Beyond static datasets, the platform provides tools for building dynamic evaluation environments that probe model behavior under distributional shift, adversarial perturbations, or compositional reasoning challenges. This is critical for agent-based AI systems that must navigate open-ended tasks, execute multi-step plans, and recover from errors. Snorkel’s research origins at the Stanford AI Lab underpin its approach: the team has spent nearly a decade shaping