data partnerships

Activate first-party data without the data sharing headache

Setting up privacy-compliant data partnerships just got a whole lot easier. Matching of cohorts, lookalike discoveery, calculation of aggregated metrics and much more, all without any partner's PII data leaving their firewall.

A federated network of Trusted Research Environments to enable safe analytics

By acting as the link within the Trusted Research Environment’s ecosystem, Bitfount enabled secure analyses by training and deploying of AI models.

key features and benefits

Manage your partnerships safely while
creating maximum value for all sides

No data leaves

No data anonymisation required

Certifiably secure

HIPAA & GDPR compliant and ISO27001 certified

Install in minutes

Install via desktop app or Python SDK

Flexible data integrations

Analyse structured and unstructured data

Custom results

Visualise results with custom PDF reports

CAPABILITIES

Relevant capabilities for data partnerships

Federated inference

Run models on remote datasets or pull down others’ models

Federated analytics

Distribute analytics queries and safely combine the results

Secure aggregation

Combine analysis results without revealing individual contributions

Frequently asked questions

Can Bitfount access my data?

No, Bitfount’s zero-trust architecture ensures Bitfount never receives data or analysis results. Analysis results are transferred between data custodians and data scientists end-to-end encrypted using encryption keys held by each party and not accessible to Bitfount. Read more here.

What will my Information Governance team think of this?

Information Governance and Legal teams love Bitfount since it removes the need for complex Data Sharing Agreements (DSA) and Material Transfer Agreements (MTA), and massively simplifies Data Protection Impact Assessments (DPIA). Bitfount is also fully GDPR, HIPAA and ISO27001 compliant. Visit our online Trust Centre here.

Do I need to be able to code?

No. Our desktop application has been designed to be operated without requiring any coding knowledge. This includes connecting datasets, joining projects, running tasks and accessing analysis results. Data scientists and algorithm developers can choose to interact with Bitfount via our python SDK.

Are there any hardware requirements?

There aren’t any specific requirements, however AI analysis at scale can be compute-intensive and will run more efficiently, especially for image analysis, if you have access to a machine (physical or virtual) with a GPU. Suitable devices include any Apple silicon model, or a Windows or Linux machine with an Nvidia GPU. Not sure if you have the right setup? Reach out to us at support@bitfount.com for guidance.

Still have questions?

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