Snorkel AI
Expert data development and specialized agents for frontier AI models
Enterprise·Technical
Key strengths
Research-grade, expert-curated training datasets for frontier AIRigorous calibration pipelines with full label provenance and audit trailsCustom benchmark and evaluation harness developmentSpecialized AI agent development grounded in domain-specific dataDeep academic roots — founded out of Stanford AI Lab with peer-reviewed research
Enterprise pricing
Redwood City, USA
Founded 2019
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- RLVR (Reinforcement Learning from Verifiable Rewards) – Generate verifiable outcome datasets across low-data and compute-constrained regimes for RL-based fine-tuning
- Custom benchmark development – Design and build bespoke evals with deterministic graders, difficulty tiers, and runnable environments targeting specific model failure surfaces
- Continual learning evaluation – Create expert-validated multi-task sequences for agents that learn across task sequences rather than isolated prompts
- Agentic environment construction – Build browser/GUI harnesses, CLI tool environments, and multi-step stateful workflows for agent training and evaluation
- Rubric-based automated evaluation – Apply the RIFT (Rubric Failure Mode Taxonomy) framework to diagnose and fix broken evaluation rubrics in production model pipelines
- Data-as-a-Service for model training – Source curriculum-structured datasets (Snorkel Data Series) with built-in reviewer guidance, difficulty tiers, and eval slices for targeted capability improvement