Datasets and environments that give frontier models domain expertise
Snorkel builds the human expert-authored datasets, evaluation environments, and benchmarks calibrated to push the limits of frontier model capability. Off-the-shelf or custom.
Where generic data runs out
Frontier model development stalls on data problems generic pipelines weren't built to solve, including distributional gaps in specialized domains, benchmark blind spots, and failure modes that only surface at scale. We build the data to solve them.
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Two ways to get the data you need
What the data frontier models need most is rarely the data that already exists. Snorkel delivers it two ways: off-the-shelf for well-defined task areas, or custom-built for the gaps only you can see.
Curriculum-structured datasets for the task areas frontier models are pushing hardest, with rubrics, reviewer guidance, difficulty tiers, and eval slices built in.
Bespoke datasets, evaluation environments, and benchmark expansions to target the exact failure surface you're trying to close.
Built for the task areas that matter now
Co-developed with leading frontier AI teams. Each series is curriculum-structured to build difficulty progressively across a task area, with the evaluation infrastructure to match. A look at a few areas we support:
Agentic coding
Terminal tasks
Enterprise RL environments
Multimodal STEM
Specialized computer use agents
Closing gaps existing datasets can’t reach
Custom data development engagements start with the failure surface: what the model can't do, where it's brittle, and what the correct evaluation criteria are. From there, Snorkel builds the datasets, environments, and benchmark expansions needed to close it.
Research-validated methodology
