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.
Agents’ Last Exam V1.1
Evaluating AI agents on long-horizon, economically valuable professional workflows with verifiable outcomes.
OSWorld 2.0
Evaluates computer-use agents on 108 long-horizon workflows.
Terminal-Bench 4.1
Evaluates terminal agents on continuously updated software-engineering tasks.
Terminal-Bench 4.0
Evaluates terminal agents on continuously updated software-engineering tasks.
Agentic Coding 2.0
Evaluating whether coding agents can plan, execute, verify, and recover across complex terminal-native engineering tasks.
Senior SWE-bench
Evaluates coding agents on senior-level software engineering tasks.
WorkplaceAgents
Evaluates AI agents on economically valuable professional work and verifiable workplace deliverables.
SlopCode Bench
Measures coding-agent performance across evolving software requirements.
SnorkelRevOps
Evaluates revenue-operations workflows involving systems, controls, and business-state updates.
Continual Learning Bench
Measures improvement across sequential, stateful tasks.
SnorkelManufacturing
Evaluates manufacturing decisions using fragmented plant-floor, engineering, and supplier evidence.
SnorkelFinance 2.0
Evaluates financial workflows requiring evidence gathering, analysis, and compliance.
Terminal-Bench 3.0
Evaluates terminal agents on containerized tasks across seven domains.
SWE-bench CLI
Evaluates validated multi-file code changes in open-source repositories.
SnorkelLegal
Evaluates legal workflows requiring evidence, procedure, authority, and action.
Terminal-Bench-Science
Evaluates agents on scientific workflows derived from researchers’ own work.
MBABench 2.0
Evaluates agents on building financial models in spreadsheets, across 101 expert-written tasks.
SnorkelUnderwrite 2.0
Evaluates auditable insurance underwriting decisions using incomplete, distributed evidence.
LibraryDesignBench
Measures how well AI agents design libraries that other agents can use.
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.