Snorkel AI
Snorkel AI is a research-driven company that specializes in expert data development for frontier AI. They help frontier AI labs and enterprise teams develop specialized training data and environments that push the boundaries of what AI models and agents can achieve.
What They Do
Snorkel focuses on two core offerings:
Data Development
Snorkel provides expert-curated datasets designed specifically for frontier AI models. Their approach combines research-led methodologies with domain expertise to create high-quality training data that improves model performance on complex tasks.
Specialized Agents
They build custom AI systems tailored to unlock ROI quickly for enterprise customers. These specialized agents are designed to solve specific business problems with precision.
Research & Innovation
Snorkel is deeply rooted in academic research, with partnerships spanning top institutions like Stanford University, University of Wisconsin, Brown University, and University of Washington. They contribute to open benchmarks and maintain leaderboards for evaluating AI model performance, including their notable Agentic Coding Benchmark for evaluating AI models on complex, real-world coding tasks.
Trusted Partners
Snorkel proudly partners with leading frontier AI organizations including Google, Microsoft, Amazon Web Services, OpenAI, Anthropic, and Mistral, demonstrating their credibility and importance in the AI ecosystem.
Expert Community
They maintain an active expert community where domain specialists can get paid to contribute to shaping safer, smarter AI systems.
Target Audience
Frontier AI labs, enterprise AI teams, research institutions, and organizations developing large-scale AI models requiring specialized training data.
Key Features
- Expert-curated training datasets for frontier AI models
- Specialized custom AI agent development
- Research-led data development methodology
- Agentic Coding Benchmark for evaluating AI models
- Open benchmarks and leaderboards for model comparison
- Expert community for domain specialists
- Terminal-Bench 2.0 evaluation framework for AI coding agents
- Partnerships with top AI labs and research institutions
Use Cases
- Developing specialized training data for frontier language models
- Building custom AI agents for enterprise automation
- Evaluating AI model performance on complex coding tasks
- Creating expert-curated datasets for domain-specific AI applications
- Benchmarking and comparing model capabilities across tasks
- Improving model alignment and safety through quality data
Pros
- Trusted by leading AI labs (OpenAI, Anthropic, Google, Microsoft)
- Strong research foundation with academic partnerships
- Expert-curated data ensures high quality training sets
- Custom solutions tailored to specific business needs
- Active contribution to open-source AI research and benchmarks
Cons
- Enterprise-focused pricing may not be accessible to small teams
- Specialized service model rather than self-serve platform
- Primarily targets frontier AI labs and large organizations
Pricing
Enterprise pricing - contact sales for custom quotes. Expert community contributors are paid for their work.
