Snorkel AI

Snorkel AI provides an end-to-end data development platform specifically designed for frontier AI teams working with complex models and agentic systems. The platform operationalizes the full AI data loop—from dataset curation and realistic simulations to rubric design and evaluations. It helps teams design, stress-test, evaluate, and improve the data powering their frontier models and agent behavior through a structured process of planning, execution, refinement, and evaluation.

What sets Snorkel AI apart is its expert-in-the-loop approach that combines programmatic automation with calibrated human expertise. Using rubrics, verifiers, and review loops, the platform helps AI teams curate high-quality datasets 2× faster without sacrificing volume or precision. The system covers 1,000+ expert-level topics and provides meta-evaluation capabilities, evaluator development tools, and both model-based and rule-based evaluation methods. This allows teams to analyze failures, update rubrics, and target data collection to close coverage gaps systematically.

The platform is particularly valuable for AI teams facing challenges like shifting targets, edge cases, uneven quality, and one-off evaluations. It's designed for organizations that need reproducible results, realistic simulations, and terminal-grade coding tasks to measure AI behavior accurately. Snorkel AI's research-backed approach—with team members from Stanford, MIT, and UC Berkeley—provides benchmarks and datasets that teams can audit, reproduce, and build upon for their frontier AI applications.