We build the data that pushes the frontier
Snorkel helps frontier labs and AI teams develop specialized training data and environments that set their models and agents apart.
Frontier models break at the edges. We build for that.
Founded out of Stanford AI Lab, we've been shaping and benchmarking frontier AI for nearly a decade.
Research Collaboration
Continual Learning Bench
Expert-validated tasks for agents that learn across task sequences, not isolated prompts.
Agentic Coding
Terminal-Bench 2.0
Real terminal tasks — exposing where today's coding agents fail.
Data development for the frontier
Snorkel partners with frontier AI teams to build research-grade datasets, evaluation systems, and runnable environments where generic coverage runs out.
Snorkel Data Series
Custom data development
Specialized agents
Expert Demonstrations & Reasoning
Preference Labels & Rankings
Rubrics & Verifiable Outcomes
Standard & Custom Environments
Good data is a set of design choices
Most data quality problems are design problems. Ambiguous task definitions produce inconsistent labels. Uncalibrated reviewers introduce systematic bias. Missing provenance makes failure analysis guesswork. Snorkel's proprietary process is built around the decisions that determine whether training data actually drives model improvement:
Specialized agents grounded in expert data
The same data development system we use to improve frontier models powers our specialized agents. That means agents evaluated against task-specific rubrics and programmatic checks – not generic benchmarks – and refined through the same adjudication and provenance practices used in production model development.
Research that shapes the work
Every dataset, benchmark, and environment we create is the output of active research co-developed and peer-reviewed with leading academic teams and frontier labs.
