Snorkel AI

Snorkel AI is a frontier AI data lab that partners with leading AI teams to build research-grade datasets, evaluation systems, and runnable environments where generic coverage runs out. Founded out of the Stanford AI Lab in 2019, Snorkel started with a contrarian bet: that training data, not models or compute, would decide whether machine learning worked. Their data programming and weak supervision approach, introduced in their 2017 VLDB paper, has since evolved into a platform offering expert-curated datasets, custom data development, and specialized agent systems. The company serves frontier labs, enterprise AI teams, and researchers who need high-quality, domain-specific data for the hardest AI problems. Their Data Series provides curriculum-structured datasets with rubrics, reviewer guidance, difficulty tiers, and eval slices. They also offer bespoke data development and benchmark tools like Continual Learning Bench (with UC Berkeley) and Agents' Last Exam (with Berkeley RDI and 300+ experts). What makes Snorkel different is its research pedigree and focus on data-centric AI. They have published 250+ papers, won awards at NeurIPS, ICML, ICLR, and VLDB, and maintain open benchmarks and grants for open-source AI research. Their platform combines expert contributors, custom pipelines, and evaluation frameworks to address distributional gaps and tasks where correctness is hard to define. Pricing is not publicly listed; the company offers Data-as-a-Service and custom solutions via consultation. They target advanced users (researchers and engineers) who need specialized data for frontier models, not general-purpose data labeling tools.