List of Snorkel Flow Customers
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United States
Since 2010, our global team of researchers has been studying Snorkel Flow customers around the world, aggregating massive amounts of data points that form the basis of our forecast assumptions and perhaps the rise and fall of certain vendors and their products on a quarterly basis.
Each quarter our research team identifies companies that have purchased Snorkel Flow for ML and Data Science Platforms from public (Press Releases, Customer References, Testimonials, Case Studies and Success Stories) and proprietary sources, including the customer size, industry, location, implementation status, partner involvement, LOB Key Stakeholders and related IT decision-makers contact details.
Companies using Snorkel Flow for ML and Data Science Platforms include: Chubb, a Switzerland based Insurance organisation with 34000 employees and revenues of $40.96 billion, SLB, formerly ChampionX, a United States based Professional Services organisation with 111000 employees and revenues of $36.29 billion, Memorial Sloan Kettering Cancer Center, a United States based Healthcare organisation with 21838 employees and revenues of $6.63 billion and many others.
Contact us if you need a completed and verified list of companies using Snorkel Flow, including the breakdown by industry (21 Verticals), Geography (Region, Country, State, City), Company Size (Revenue, Employees, Asset) and related IT Decision Makers, Key Stakeholders, business and technology executives responsible for the software purchases.
The Snorkel Flow customer wins are being incorporated in our Enterprise Applications Buyer Insight and Technographics Customer Database which has over 100 data fields that detail company usage of software systems and their digital transformation initiatives. Apps Run The World wants to become your No. 1 technographic data source!
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| Logo | Customer | Industry | Empl. | Revenue | Country | Vendor | Application | Category | When | SI | Insight |
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Chubb | Insurance | 34000 | $41.0B | Switzerland | Snorkel AI | Snorkel Flow | ML and Data Science Platforms | 2021 | n/a |
In 2021, Chubb deployed Snorkel Flow to support insurance document processing in the United States. Chubb deployed Snorkel Flow, an ML and Data Science Platforms solution, to manage complex labeling workflows for document classification and information-extraction tasks.
The implementation focused on programmatic labeling capabilities, including instrumentation to manage noisy labels and reviewer collaboration workflows to scale labeling across multiple use cases. Snorkel Flow was used to author and operationalize labeling functions and to coordinate human review, enabling scalable training data creation for document classification and information-extraction modules.
Operational scope targeted insurance document processing across Chubb's United States operations, where Snorkel Flow was embedded into data-centric model development practices. The deployment aligned the Snorkel Flow application with existing document classification and information-extraction pipelines, supporting iterative model training and labeling lifecycle management.
Governance and process changes emphasized reviewer collaboration and label quality management, formalizing workflows for programmatic labeling and noisy label remediation. The work enabled Chubb to accelerate data-centric model development and operationalize programmatic labeling for document classification and information-extraction tasks using Snorkel Flow.
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Memorial Sloan Kettering Cancer Center | Healthcare | 21838 | $6.6B | United States | Snorkel AI | Snorkel Flow | ML and Data Science Platforms | 2022 | n/a |
In 2022, Memorial Sloan Kettering Cancer Center implemented Snorkel Flow, an ML and Data Science Platforms application, to programmatically label pathology reports. The deployment targeted pathology-report labeling tasks and was designed to programmatically label thousands of reports, reducing subject matter expert labeling time while preserving classification quality in clinical documentation workflows.
Snorkel Flow was used to author and manage programmatic labeling functions and to orchestrate data-centric labeling pipelines, enabling weak supervision and iterative model training with human-in-the-loop validation. The implementation emphasized labeling function development, probabilistic label modeling, and label conflict resolution as core capabilities of Snorkel Flow, supporting automated label propagation and repeatable data preparation for downstream models.
Operational coverage included pathology, clinical informatics, and data science teams within the United States context, with workflows that embedded SME review and iterative validation into model training cycles. Governance focused on structured human-in-the-loop validation and iterative labeling governance to ensure clinical accuracy. Publicly reported outcomes for the pathology labeling use cases included about 95% accuracy and approximately 85% precision, alongside a dramatic reduction in SME labeling time.
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SLB, formerly ChampionX | Professional Services | 111000 | $36.3B | United States | Snorkel AI | Snorkel Flow | ML and Data Science Platforms | 2023 | n/a |
In 2023, SLB implemented Snorkel Flow to build named entity recognition and information-extraction pipelines over geological and well-maintenance reports. The deployment used Snorkel Flow within ML and Data Science Platforms to enable domain experts and data scientists to iterate rapidly on labeling and model development in the United States energy context.
Snorkel Flow was configured to capture labeling expertise as programmatic labeling functions, structuring weak supervision workflows that replaced manual annotation bottlenecks and accelerated training data generation for NER and information extraction. The implementation emphasized automated labeling pipelines and iterative model retraining, enabling subject matter experts to encode heuristics and rules as reusable programmatic functions.
Operational coverage focused on processing geological and well-maintenance documents, with the solution supporting collaboration between domain experts and data science teams in the energy industry. The scope targeted content extraction and information pipelines rather than enterprise system integrations, enabling rapid prototyping directly on source report sets.
Governance shifted toward labeling function lifecycle management and iterative validation, with domain knowledge captured in code to standardize labeling decisions across teams. The engagement delivered a production-capable Snorkel Flow solution in under three days and achieved approximately 91 F1 after iteration, accompanied by large reductions in per-report processing time.
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Buyer Intent: Companies Evaluating Snorkel Flow
- ByteDance France, a France based Professional Services organization with 200 Employees
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