Snorkel AI
UnknownExpert-curated training data, benchmarks, and evaluation environments for frontier AI models and agents.
Информация об инструменте
Что такое Snorkel AI?
Snorkel AI builds specialized training data, benchmarks, and evaluation environments that help frontier models and agents perform in high-stakes domains through research-led data development. Founded out of Stanford AI Lab, the company partners with frontier AI teams to create research-grade datasets, evaluation systems, and runnable environments where generic coverage runs out.
Основные функции
Data development
Research-led data and environment development including expert-curated datasets for frontier AI, with curriculum-structured datasets, rubrics, reviewer guidance, difficulty tiers, and eval slices.
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
Bespoke datasets, evals, and benchmark expansions built for specific failure surfaces when off-the-shelf coverage runs out.
Specialized agents
Custom AI systems and agents built on specialized data and evaluated in real workflows, with pass/fail criteria tied to performance standards that move ROI.
Expert demonstrations and reasoning
Human solution traces, reasoning traces, SME Q&A rationales, workflow demos, decision workflows, and tool-use demos for training and evaluation.
Preference labels and rankings
Preference labels and rankings to support alignment and model improvement.
Rubrics and verifiable outcomes
Rubrics and verifiable outcomes for evaluation of model and agent performance.
Standard and custom environments
Runnable standard and custom environments for evaluating models and agents on complex tasks.
Agentic data and evaluation workflows
Structured data and evaluation workflows for agents that need to make decisions, use tools, and complete complex multi-turn, multi-tool tasks end to end.
Annotation workflows
Annotation workflows designed for tasks where quality depends on domain expertise, clear review standards, and consistent judgement.
Варианты использования
- #1
Agentic
Structured data and evaluation for tool-using systems and agents handling multi-turn math reasoning, multi-agent conversations with tool use, robust evaluation benchmarks, and multi-step deep research data.
- #2
Annotation
Expert-labelled annotation for grading LLM information retrieval and synthesis from technical documents, and enabling foundation models to understand charts.
- #3
Coding
Alignment data for better code generation and training/evaluation data for code generation models and coding agents.
- #4
Multi-modal
Data to enable foundation models to understand charts and other multi-modal content.
- #5
Text generation
Data development supporting text generation capabilities for frontier models.
Тарифные планы
No public pricing details available; engagement appears to be through custom data development and expert services via contact or talk-to-an-expert forms.
Альтернативы
Часто задаваемые вопросы
What is Snorkel AI?
What kinds of data and services does Snorkel AI offer?
Who is Snorkel AI designed for?
What are common use cases for Snorkel AI?
Is public pricing available for Snorkel AI?
How can I contact Snorkel AI or learn more?
Snorkel AI Контакты и социальные сети
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