how bitfount works
Federated data science enables organisations to collaborate easily, quickly and confidently by sending algorithms to data, not the other way around. From GenAI, to image analysis, to advanced multi-party analytics.
how bitfount works
Bitfount supports a range of AI and data science capabilities out of the box, all accessible in a single, flexible platform depending on the needs of your project.
Bitfount supports a range of AI and data science capabilities out of the box, all accessible in a single, flexible platform depending on the needs of your project.
Federated inference
Bring the models to the data. Bitfount’s governance layer makes managing model usage permissions a breeze.
Private fine-tuning
Customise open-source models to achieve high accuracy on your domain tasks, without having to transfer your data out to third parties.
Federated training
Only model weights are transferred and aggregated, giving you the best models with minimal governance burden.
Federated model evaluation
Send models to truly held-out private datasets and receive back just the performance metrics.
Federated analytics
Run complex analytics to address your toughest research, BI and data integrity questions, all without any data transfer.
Private set intersection
Activate your first party data from your own warehouse without the need for third-party data clean rooms or confidential computing enclaves.
Longitudinal analysis
Unlock insights on disease progression, treatment efficacy and more by analysing real-world patient-level imaging and EHR data.
Automated data transfer
Centralise data from your partners to your own on-premise or cloud storage for observational trials, data partnerships and more.
Flexible federated protocols
Full governance layer
Granular audit trail
One-off or recurring analyses
Built-in Privacy Enhancing Technologies (PETs)
No-code app and Python SDK
Zero-trust architecture
differential privacy
Ensure analysis results cannot be used to reconstruct
the original data.
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secure aggregation
Enable confidential benchmarking, cross-organisation BI queries, and federated model training.
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