Snorkel AI is an enterprise-focused artificial intelligence (AI) platform provider that programmatically streamlines data development processes for AI systems. The company is based in Redwood City, California, and originated from Snorkel Research, an academic project initiated in 2015 at the Stanford AI Lab. Its primary goal is to help organizations build custom AI models using proprietary data.

Founding and Origins

Snorkel AI was founded on academic research that focused on weak supervision methods. These efforts involved collaboration with institutions such as Google, Intel, and the U.S. Defense Advanced Research Projects Agency (DARPA). What began as a research initiative under the name Snorkel Research evolved into a commercial platform under the name Snorkel AI. Among the company’s founders is Alex Ratner, a key figure in the development of the original academic framework.

Snorkel Flow Platform

The company’s flagship product, Snorkel Flow, enables enterprises to transform unstructured data into training-ready formats for AI systems. It centralizes data labeling, model training, evaluation, and fine-tuning through programmatic methods. Snorkel Flow supports both predictive machine learning and generative AI applications.

The platform offers a wide range of functionalities for domain experts and data scientists, including domain-specific LLM (Large Language Model) evaluation tools, Retrieval-Augmented Generation (RAG) workflows, Named Entity Recognition (NER) on PDFs, UI enhancements, and data slicing techniques. Snorkel Flow integrates with technologies such as Databricks, Amazon SageMaker, OpenAI’s ChatGPT, Google Gemini, and Meta LLaMA.

Data-Centric AI Approach

Unlike model-centric approaches that prioritize architecture design, Snorkel AI follows a data-centric paradigm, emphasizing data quality. The platform allows domain experts to encode their knowledge into programmatic labeling functions, making training data creation more systematic. Data workflows are versionable, editable, and reusable, mirroring software development practices.

Use Cases

Snorkel Flow is employed by organizations across sectors including banking, insurance, public services, healthcare, and e-commerce. Its applications include document classification, customer interaction analysis, catalog tagging, natural language processing (NLP), and information extraction. Notable users include BNY Mellon, Wayfair, Chubb, and the U.S. Air Force.

Research and Development

With deep academic roots, Snorkel AI’s founders and collaborators have contributed to over 170 peer-reviewed papers presented at conferences such as NeurIPS, ICML, and ICLR. These works focus on areas like weak supervision, programmatic labeling, foundation model evaluation, and data slicing. The company also organizes SnorkelCon, a user conference that highlights case studies and research in data-centric AI.

Recent updates to Snorkel Flow are aimed at accelerating the development of domain-specific AI systems for enterprise users. These include custom evaluation tools for LLMs, structured document extraction functions, improved interfaces for gathering expert feedback, and new visual tools to analyze error modes in sequence labeling workflows.

Future Outlook

Snorkel AI is committed to building repeatable, traceable, and centralized pipelines for data preparation and evaluation. Future developments are expected to enhance the platform’s ability to support hybrid labeling techniques, domain-aligned evaluation metrics for generative AI, and feedback loops from domain experts. The company continues to promote a data-centric approach, emphasizing reliability, transparency, and data representation in AI system development.