List of Gretel Cloud Customers
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Since 2010, our global team of researchers has been studying Gretel Cloud 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 Gretel Cloud 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 Gretel Cloud for ML and Data Science Platforms include: Ernst & Young, a United Kingdom based Professional Services organisation with 406209 employees and revenues of $51.20 billion, SA Health, a Australia based Government organisation with 46100 employees and revenues of $5.06 billion, databricks, a United States based Professional Services organisation with 5500 employees and revenues of $1.60 billion and many others.
Contact us if you need a completed and verified list of companies using Gretel Cloud, 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.
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| Logo | Customer | Industry | Empl. | Revenue | Country | Vendor | Application | Category | When | SI | Insight |
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databricks | Professional Services | 5500 | $1.6B | United States | Gretel | Gretel Cloud | ML and Data Science Platforms | 2024 | n/a |
In 2024, Databricks integrated Gretel Cloud using a connector to embed synthetic-data workflows directly inside its Databricks environment. Gretel Cloud is positioned in the ML and Data Science Platforms category and was employed to generate safe training datasets to support model development workflows within Databricks.
The implementation surfaced Gretel Cloud capabilities as an in-workspace data generation service callable from notebooks and model training pipelines, enabling teams to provision synthetic datasets for training and experimentation. Functional capability emphasis included synthetic-data generation, dataset provisioning for model training, and workflow orchestration to incorporate generated data into iterative ML workflows.
Integration scope was focused on ML and model training use cases in the United States, notably applied to text-to-SQL model work. Teams integrated synthetic-data generation into their model development pipelines and adjusted workflows to invoke the connector prior to training runs. Reported outcomes from the integration included improvements to model iteration speed and model performance, alongside the operational benefit of producing safe training datasets for development and testing.
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Ernst & Young | Professional Services | 406209 | $51.2B | United Kingdom | Gretel | Gretel Cloud | ML and Data Science Platforms | 2024 | n/a |
In 2024, Ernst & Young implemented Gretel Cloud to add synthetic data capabilities to its enterprise AI program. Gretel Cloud, categorized as ML and Data Science Platforms, was deployed using Gretel Navigator to generate high-quality synthetic tabular and text data for model training and to accelerate product roadmaps.
The implementation focused on supporting EY's client-technology AI initiatives in a global context, with use cases explicitly tied to enterprise AI and model training workflows. Gretel Navigator was used to produce on-demand synthetic datasets for both tabular and text modalities, enabling iterative model fine-tuning and experimentation without reliance on sensitive production data.
Functionally the deployment centered on synthetic data generation, dataset provisioning for training and validation, and text and tabular data modeling capabilities provided by Gretel Navigator within Gretel Cloud. The workstreams bridged product development and model engineering functions, embedding synthetic data artifacts into model training pipelines and product roadmap acceleration activities.
Governance emphasis in the engagement was preservation of data privacy through synthetic data generation, and outcomes reported by EY included faster product development cycles and accelerated model fine-tuning while maintaining privacy safeguards. The deployment therefore connected Gretel Cloud capabilities to EY business functions focused on AI productization and client-facing technology initiatives.
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SA Health | Government | 46100 | $5.1B | Australia | Gretel | Gretel Cloud | ML and Data Science Platforms | 2023 | n/a |
SA Health implemented Gretel Cloud in 2023 to create privacy-preserving synthetic EMR datasets for safe data sharing and machine learning workflows. Gretel Cloud was deployed as part of an initiative categorized as ML and Data Science Platforms.
The deployment provisioned Gretel Cloud on Microsoft Azure infrastructure, ingesting electronic medical record data to generate anonymized synthetic datasets used for ML model training and clinical decision support. Functional capabilities implemented included synthetic data generation, privacy-preserving transformations, and dataset provisioning for analytics and model development.
The public-health implementation focused in South Australia targeted EMR data anonymization to enable secure cross-organizational data sharing for analytics and research. Operational scope covered clinical analytics and model development teams within SA Health, supporting workflows for clinical decision support and deidentified dataset distribution.
Governance measures emphasized patient privacy and controlled dataset provisioning to ensure safe access for internal and partner analytics. The program reportedly improved secure data access for analytics and model development while protecting patient privacy.
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