List of Google DeepMind Customers
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Since 2010, our global team of researchers has been studying Google DeepMind 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 Google DeepMind for Generative AI 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 Google DeepMind for Generative AI Platforms include: Moorfields Eye Hospital, a United Kingdom based Healthcare organisation with 2500 employees and revenues of $950.0 million, Embl-Ebi United Kingdom, a United Kingdom based Life Sciences organisation with 850 employees and revenues of $90.0 million, Royal Free Hospital School Of Medicine, a United Kingdom based Healthcare organisation with 10 employees and revenues of $6.7 million and many others.
Contact us if you need a completed and verified list of companies using Google DeepMind, 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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Embl-Ebi United Kingdom | Life Sciences | 850 | $90M | United Kingdom | Google DeepMind | Google DeepMind | Generative AI Platforms | 2021 | n/a |
In 2021, EMBL-EBI United Kingdom partnered with Google DeepMind to publish the AlphaFold Protein Structure Database, deploying Google DeepMind as the core application. The project used Google DeepMind within the Generative AI Platforms category to deliver predicted 3D structures for the human proteome, targeting structural biology and research across Europe and globally.
The implementation centered on publishing model-generated protein structure predictions and a public database of those predictions, coupled with search and retrieval capabilities for researchers. Configuration work emphasized standardized prediction outputs, metadata alignment with EMBL-EBI accession systems, and mechanisms for linking structural predictions to existing molecular and genomic records.
Integration work explicitly connected the AlphaFold Protein Structure Database to EMBL-EBI data resources, enabling cross-references between predicted structures and EMBL-EBI datasets used by life-sciences researchers. Operational scope covered structural biology and research workflows, making predictions accessible to external research institutions and internal EMBL-EBI data teams, and the collaboration went live in 2021.
The deployment dramatically increased access to protein-structure predictions and accelerated research outcomes, while enabling persistent integration with EMBL-EBI data resources for discovery workflows. Governance focused on dataset publication controls and data linkage practices to ensure consistent use of predicted structures within EMBL-EBI research pipelines.
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Moorfields Eye Hospital | Healthcare | 2500 | $950M | United Kingdom | Google DeepMind | Google DeepMind | Generative AI Platforms | 2016 | n/a |
In 2016, Moorfields Eye Hospital entered a research partnership to implement Google DeepMind for automated analysis of retinal scans. The Google DeepMind implementation aligned to the Generative AI Platforms category and targeted clinical diagnostics and referral decision support within ophthalmology services in the United Kingdom.
The work focused on developing and validating AI models for automated analysis of retinal imaging, incorporating model training, cross‑validation and diagnostic classification workflows typical of Generative AI Platforms applied to medical imaging. Functional capabilities implemented included automated lesion and pathology detection, diagnostic classification to support clinician decision making, and prioritisation logic intended to surface urgent cases for faster referral decisions.
Operational integration centered on embedding algorithm outputs into Moorfields clinical imaging pipelines and referral workflows, supporting clinical teams in diagnostics and triage across hospital departments that manage retinal disease. The research program produced clinical validation activities, and subsequent engineering work transitioned models and deployment responsibility to Google Health for wider operational rollout beyond the initial research setting.
Governance for the program was structured as a clinical research partnership with staged validation and publication milestones, culminating in clinical results published by 2018 that reported high diagnostic accuracy and indicated potential to prioritise urgent cases. Moorfields Eye Hospital Google DeepMind Generative AI Platforms clinical diagnostics work therefore moved from research validation toward broader deployment through Google Health while retaining clinical oversight and validation as core governance elements.
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Royal Free Hospital School Of Medicine | Healthcare | 10 | $7M | United Kingdom | Google DeepMind | Google DeepMind | Generative AI Platforms | 2016 | n/a |
In 2016 Royal Free Hospital School Of Medicine partnered with Google DeepMind to deploy the Streams clinical app for earlier detection and faster notification of acute kidney injury. The implementation targeted patient safety within NHS hospitals in London and positioned Google DeepMind under the Generative AI Platforms category to support clinical decision workflows.
The Streams clinical app configuration centered on automated AKI detection algorithms and real time clinician alerting, delivering notification workflows to frontline teams. Functional capabilities implemented included automated surveillance of clinical data feeds, event prioritization for rapid review, and in application messaging to support escalation and handover in acute care contexts.
Operational deployment was scoped to Royal Free London hospitals within the trust, with Streams ingesting hospital clinical data and routing alerts to on duty clinicians and specialty teams. The implementation was integrated into existing hospital information flows and notification pathways to embed the application into acute care and patient safety practices.
Royal Free Hospital School Of Medicine reported reduced clinician time spent reviewing notes as an operational benefit from the Streams deployment, and subsequent audits and regulatory reviews refined data governance arrangements around the application. Governance activity concentrated on clinical data handling, consent pathways and auditability for the Streams deployment, reflecting regulatory scrutiny that shaped operational controls.
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