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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.

Proud to partner with top frontier AI and research teams
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Frontier models break at the edges. We build for that.

Most data pipelines are built for volume, not difficulty. Frontier models fail on distributional gaps in specialized domains, benchmark blind spots, and tasks where correctness is hard to define. Snorkel is built specifically for these problems.
The Frontier AI Data Lab

Data development for the frontier

Snorkel partners with frontier AI teams to build the data, evaluation systems, and environments to improve models where generic coverage runs out.

Snorkel Data Series

Curriculum-structured datasets for the task areas frontier models are pushing hardest, with rubrics, reviewer guidance, difficulty tiers, and eval slices built in.

Custom data development

When off-the-shelf coverage runs out, we build bespoke datasets, evals, and benchmark expansions for the exact failure surface you need to close.

Specialized agents

Custom agents built on specialized data and evaluated in real workflows, with pass/fail criteria tied to the performance standards that moves ROI.
DATA DEVELOPMENT

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:

Calibrated expert review
Rubrics and programmatic checks
Well-specified expert-level tasks
Adjudication and provenance
Benchmarks and evals
Edge-case coverage
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Custom AGENTS

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.

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Built for specialized workflows and high-consequence decisions, not generic copilots
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Evaluation on environment-grounded tasks with programmatic pass/fail criteria
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Same rigor used to train frontier-class models, applied to your enterprise deployment
PUBLISHED RESEARCH

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.

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For models that need to be right. Not just good enough.