Capabilities and features
One federated platform with a full suite of AI and analytics capabilities
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.
A full-stack federated network
Bitfount supports a range of AI and data science capabilities out of the box, all easy to use and combine in a single flexible platform. Install via desktop app, SDK, or Docker on your private cloud. Learn more.
Core capabilities
Federated inference
Bring models to data to make predictions, without any data crossing the firewall
Run commercial, academic or open source AI models where data resides, behind the firewall. 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 cross-silo datasets without any data moving
With federated learning (FL) training takes place where data resides - algorithms come to data and not the other way around. Only model weights are transferred and securely 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 held-out private datasets and receive back just their performance metrics. Data custodians can understand model performance instead of relying on third-party claims. Model developers get external validation of their models and can assess the ROI of novel training data without acquiring it.
Federated analytics
Extract insights and trends across multiple, siloed datasets for data-driven decision-making
Run complex analytics to address your toughest research, BI and data integrity questions, all without any data transfer. Securely aggregate results across multiple datasets.
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
Stratify patients and gain clinical insights based on disease progression
Unlock insights on disease progression, treatment efficacy and more by analysing real-world patient-level imaging and EHR data.
Automated data transfer
Secure, automated data transfer for imaging-, EHR- and any other data
Centralise data from your partners to your own on-premise or cloud storage for observational trials, data partnerships and more.
Differential Privacy
Ensure the privacy of your results, mathematically guaranteed
Train and share powerful AI models and analytics while ensuring that results cannot be used to reconstruct the original data.
Secure aggregation
Safely aggregate results without revealing individual contributions
Enable confidential benchmarking, cross-organisation BI queries, and federated model training.
Built for secure AI collaboration.
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