how bitfount works
Let insights flow, not data
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 is a full-stack federated network
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 models to data to make predictions, without any data crossing the firewall
Bring the models to the data. Bitfount’s governance layer makes managing model usage permissions a breeze.
Private fine-tuning
Fine-tune the world’s leading open-source foundation models on your private data
Customise open-source models to achieve high accuracy on your domain tasks, without having to transfer your data out to third parties.
Federated training
Train models on multiple proprietary datasets without any data moving
Only model weights are transferred and aggregated, giving you the best models with minimal governance burden.
Federated model evaluation
Calculate model performance metrics on private data without sharing models, or data
Send models to truly held-out private datasets and receive back just the performance metrics.
Federated analytics
Extract insights and trends from multiple datasets for data-driven decision-making
Run complex analytics to address your toughest research, BI and data integrity questions, all without any data transfer.
private set intersection
Find dataset overlaps without disclosing unwanted information
Activate your first party data from your own warehouse without the need for third-party data clean rooms or confidential computing enclaves.
Bitfount is a full-stack federated network
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 models to data to make predictions, without any data crossing the firewall
Bring the models to the data. Bitfount’s governance layer makes managing model usage permissions a breeze.
Private fine-tuning
Fine-tune the world’s leading open-source foundation models on your private data
Customise open-source models to achieve high accuracy on your domain tasks, without having to transfer your data out to third parties.
Federated training
Train models on multiple proprietary datasets without any data moving
Only model weights are transferred and aggregated, giving you the best models with minimal governance burden.
Federated model evaluation
Calculate model performance metrics on private data without sharing models, or data
Send models to truly held-out private datasets and receive back just the performance metrics.
Federated analytics
Extract insights and trends from multiple datasets for data-driven decision-making
Run complex analytics to address your toughest research, BI and data integrity questions, all without any data transfer.
Private set intersection
Find dataset overlaps without disclosing unwanted information
Activate your first party data from your own warehouse without the need for third-party data clean rooms or confidential computing enclaves.
Longitudinal analysis
Find dataset overlaps without disclosing unwanted information
Activate your first party data from your own warehouse without the need for third-party data clean rooms or confidential computing enclaves.
Automated data transfer
Find dataset overlaps without disclosing unwanted information
Activate your first party data from your own warehouse without the need for third-party data clean rooms or confidential computing enclaves.
Features
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
Additional privacy-enhancing technologies
differential privacy
Ensure the privacy of your results, mathematically guaranteed
Ensure analysis results cannot be used to reconstruct
the original data.
Learn more
secure aggregation
Safely aggregate results without revealing individual contributions
Enable confidential benchmarking, cross-organisation BI queries, and federated model training.
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