Snorkel Flow is an AI data development platform that enables data scientists to programmatically label and annotate large datasets, significantly reducing the time required for data preparation. By leveraging domain knowledge and automated techniques, the platform enhances the accuracy and efficiency of training data for specialized AI applications in fields like bioinformatics and natural language processing.
Funding
$138.3M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
QVFounders
Product
Problem
Training AI models, especially for specialized applications, requires large, accurately labeled datasets, which are often time-consuming and expensive to create manually. Existing data labeling processes struggle to efficiently incorporate domain expertise, leading to bottlenecks in AI development and limiting the performance of specialized models.
Solution
Snorkel AI provides an AI data development platform that enables data scientists and subject matter experts to programmatically label, evaluate, and refine training data. The platform allows users to capture domain knowledge and apply it to entire datasets, eliminating the need for manual annotation. By building custom benchmarks and specialized evaluators, AI teams can improve knowledge retrieval, agent tool use, and LLM generation through prompt engineering, RAG optimization, and model fine-tuning. Snorkel AI's approach accelerates the curation of high-quality training data, delivering specialized LLMs that meet production accuracy requirements, ethical standards, company policies, and industry regulations.
Target Audience
Snorkel AI targets data scientists, subject matter experts, and AI/ML engineers in enterprises across various industries, including banking, finance, healthcare, insurance, and the public sector, who are building specialized AI applications.
Features
- Programmatic data labeling and annotation using domain knowledge
- Custom benchmark creation for AI system evaluation
- Specialized evaluator development for fine-grained error analysis
- Tools for prompt engineering, RAG optimization, and LLM fine-tuning
- Capabilities for knowledge retrieval, agent tool use, and content generation
- Integration with existing AI/ML stacks
- Expert Data-as-a-Service for custom, high-quality dataset delivery