your frontier data factory
Better data is built, not collected
Snorkel combines task design, programmatic checks, calibrated expert review, and realistic evaluation environments to create measurable training signal for frontier models and agents.
We build for the edges of the frontier
Frontier models stall on specialized tasks, benchmark blind spots, and failure modes that only show up at the edges. Snorkel builds the data, evals, and environments needed to close those gaps.
how we work
Measure where models break
Curate data and environments against those failures. Refine the system until performance improves. Repeat.
01
Evaluate
Measure behavior against task-specific benchmarks inside realistic environments, with programmatically defined pass/fail criteria.
02
Curate
Run rubric-guided pipelines with calibrated experts in the loop, including environment construction with the tools, documents, and verifiable reward signals agents are rigorously evaluated against.
03
Refine
Analyze disagreements, trace failures, and map coverage gaps. Update rubrics, expand benchmarks, and target the next collection cycle for underperforming slices.
EXPERT-IN-THE-LOOP
Programmatic scale. Human precision. Together.
Every dataset Snorkel builds is shaped by domain experts who understand the real-world context models will operate in. The result is training signal that reflects how decisions are actually made, not just how they look on the leaderboard.
1,000+ expert-level domains covered
Meta-evaluation
We evaluate our evaluators. Reviewer calibration is measured and corrected, not assumed.
Evaluator development
Model-based and rule-based evaluators trained on expert-adjudicated data, improving alongside the underlying models.
Expert correction and feedback
Every disagreement is adjudicated and fed back into the rubric, creating a documented record of where the quality standard was sharpened.
RESEARCH-VALIDATED
Our methodology is published. The results are reproducible.
Our research team, drawn from Stanford, MIT, and UC Berkeley, works directly on the methodology behind the production system, across 200+ peer-reviewed papers and open benchmarks.
01
Benchmark and eval design published and peer-reviewed
02
Evaluator development and calibration methodology documented
03
Reproducible traces and failure analysis available for partner teams
04
Research collaboration and co-publication with frontier labs teams
Get started
Two ways to work with Snorkel’s Data Lab
We build what closes the gap: expert-authored datasets and environments, delivered through the Snorkel Data Series or built custom for your task area.
Data development
Ready-to-use datasets from the Snorkel Data Series, or custom data development for domain-specific tasks, benchmark expansions, and edge case coverage.
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Specialized agents
Custom agents built on specialized datasets and evaluated against real workflow requirements using the same data development loop.
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