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Federated Confidential Cloud for Responsible AI

Building trust in zero-trust world

GENXT delivers solutions for cloud transition and multi-party collaboration, ensuring data, software, and AI models are accessible only to authorised parties

In biomedical research and beyond

Designed to empower digital rights protection across computational life sciences infrastructures, biobanks, and AI-driven applications

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Confidential Cross-Database Analysis

Perform computations on data from multiple sources where each data controller independently manages collaboration policies and retrieves computational results only

Confidential AI Inference

Provide or leverage any AI solution in clouds without the risk of private data leakage

Confidential Model Training

Train machine learning models on one or several data sources without seeing the data and disclosing both ML code and trained model

Confidential Model Licensing

Deploy machine learning models or software tools without their disclosure and the loss of control over their usage. Licensors have no access to the client input, are able to track usage statistics and deactivate the service when necessary

GENXT solutions are built on Confidential Computing and GA4GH standards

Data is always encrypted and undisclosed during storing, transferring, and processing

Fast processing of high-volume datasets

Compatible with a broad technology stack that facilitates a wide range of data processing scenarios including machine learning

Provides interoperability as it implements the Global Alliance for Genomics and Health (GA4GH) API standards

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GENXT as part of leading communities

GENXT is proud to be a resident of the Wellcome Genome Campus in the UK, home to the world’s foremost organisations and institutions in genomics and computational biology.

As general member of the Confidential Computing Consortium and Linux Foundation, GENXT contributes to the adoption of confidential computing technology in genomics.

As a GA4GH member, GENXT endorses its best practices, policies, standards and frameworks with respect to handling highly sensitive data.

Memberships & Affiliations