- Pricing
- enterprise
- From
- See site
- Free tier
- No
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Best for
Frontier AI labs and enterprise AI teams that need expert-curated training data, evaluations, and environments for specialized or high-stakes tasks.
What it is
Snorkel AI develops training data, evaluation systems, and runnable environments for frontier AI labs and enterprise AI teams. Its services cover expert-curated datasets, custom data development, and specialized agents built on that data. The company also publishes benchmarks and research on agent evaluation and data-centric AI.
Best forFrontier AI labs and enterprise AI teams that need expert-curated training data, evaluations, and environments for specialized or high-stakes tasks.
Features
Expert-curated datasets
Datasets built for specific model failure modes, with task specs, acceptance criteria, and verifier definitions written before data work begins.
Custom data development
Bespoke datasets, evaluations, and benchmark expansions for task areas where off-the-shelf coverage is insufficient.
Calibrated expert review
Reviewers trained against gold sets, scored for agreement and bias, and re-calibrated per task.
Rubrics and programmatic checks
Rubrics co-designed with domain experts and distilled into programmatic graders and evaluator models.
Adjudication and provenance
Author, multi-reviewer, and final-adjudicator pipeline with audit trails tracing each label to who decided what, when, and on what evidence.
Environments
Standard and custom environments including repo and CLI tools, browser/GUI harnesses, multi-step stateful workflows, and simulated environments.
Specialized agents
Custom agents built on specialized data and evaluated in real workflows with pass/fail criteria tied to performance standards.
Benchmarks and evaluations
Eval harnesses with task-specific rubrics, deterministic graders, and runnable environments producing reproducible scores across model versions.
Snorkel AI builds expert-curated training data, evaluations, and environments for frontier AI models and specialized agents.
Pros
- •Founded out of the Stanford AI Lab and has worked on frontier AI data for nearly a decade.
- •Publishes benchmarks and research, including Terminal-Bench, Senior SWE-bench, OSWorld 2.0, and Agents' Last Exam.
- •Offers both data development services and custom specialized agents.
- •Runs an expert contributor community that pays domain experts for contributions.
Cons
- •No public pricing; the pricing page returns a 404, so costs are only available by contacting sales.
- •No self-serve product or free tier is described on the site.
- •Services are aimed at frontier labs and enterprises, not individual developers or small teams.
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