Snorkel AI’s flagship platform uses automation and repeatable workflows to transform disparate, siloed data and domain knowledge into high‑quality data for training and evaluating AI models. Its data‑centric approach is designed to accelerate safer, faster deployment of specialized AI systems, particularly in complex and regulated environments. Product offerings include Snorkel Expert Data‑as‑a‑Service and Snorkel Enterprise AI to accelerate evaluation and tuning of specialized models and help teams move from prototype to production. The company emphasizes embedding expert knowledge directly into the AI development process to improve labeling and data curation. Snorkel plans to collaborate with Accenture to build tailored, industry‑specific solutions, with an initial focus on financial services. Its platform is used in production by Fortune 500 companies including BNY and Experian, as well as by the U.S. government. Snorkel AI provides a programmatic AI data development platform that helps enterprises build, evaluate, and tune specialized AI systems and agents for production. The company announced general availability of two offerings—Snorkel Evaluate and Snorkel Expert Data-as-a-Service—to scale fine-grained evaluation and deliver white-glove expert datasets. Snorkel Evaluate offers programmatic tooling for benchmark dataset creation, specialized evaluators, and error-mode correction to move beyond generic benchmarks and off-the-shelf judgment approaches. Snorkel Expert Data-as-a-Service pairs Snorkel’s programmatic technology with a network of trained subject-matter experts to produce datasets for advanced reasoning, agentic tool use, multi-turn interaction, and domain-specific knowledge. Snorkel says its platform is used in production by Fortune 500 customers (including BNY, Wayfair, and Chubb) and by U.S. federal customers such as the U.S. Air Force. The company will use new capital to expand engineering, research, and go-to-market efforts while advancing evaluation and tuning of specialized AI with expert data. Snorkel AI began as a research project in 2015 and was established as a company in 2019. Its core product, Snorkel Flow, converts manual AI development processes into programmatic solutions that make unstructured and unlabeled data usable for machine learning. The platform is designed to enable faster AI development and to make data accessible even to non-technical stakeholders. Snorkel addresses the insurance industry challenge where an estimated large portion of data is unstructured and not AI-ready. The company is positioned to help insurers customize models and fine-tune foundational models in more scalable and cost-effective ways. QBE’s data science and claims analytics teams in North America are using Snorkel Flow across predictive analytics applications, notably in claims and underwriting. Snorkel AI provides a programmatic data-labeling platform that minimizes hand-labeling by letting users annotate and manage data via SDKs and no-code interfaces, enabling iterative model training and error-mode identification. Its Application Studio hosts templates for common AI tasks such as contract intelligence, news analytics, customer interaction routing, text and document classification, named entity recognition, and information extraction, and supplies packaged preprocessors, programmatic labeling templates, and high-performance open-source models trainable on private data. The service decomposes apps into modular workflows to accelerate development and improve model performance. Founded in 2019 as a Stanford AI Lab spinout with engineers from Apple, Facebook, Google, Microsoft, and Nvidia, Snorkel counts customers and partners including Apple, Intel, Stanford Medicine, and U.S. government agencies. The company competes with Scale AI, Appen, Labelbox, Cloudfactory and incumbents like Amazon. Snorkel recently raised a Series C that values the company at $1 billion and brings total capital raised to $135 million; proceeds will be used to scale its engineering team and accelerate product development to reach more domains and use cases. Snorkel AI develops software to automate programmatic data labeling for machine learning, enabling subject-matter experts to apply labels via labeling functions. The company says its approach can cut labeling time and effort from months to hours or days depending on data complexity. It also launched Application Studio, a templated visual no-code interface with predefined components to help customers turn labeled data and models into production applications such as contract classifiers or network anomaly detectors. Snorkel's products are based on research that began at the Stanford AI Lab in 2015 and the company launched in 2019. The startup has 40 employees. Snorkel emphasizes making labeling auditable to help detect and address bias in automated models. Financially, the company reports it has now raised $50 million to date.

Total raised

$235M

Funding rounds

6

Latest round

Series D

Latest activity

May 2025