Bitfount provides a federated AI and data science platform enabling secure collaboration across life sciences and clinical research organizations. The platform allows algorithms to run directly on distributed data sources, such as imaging and EHR systems, without requiring data extraction or sharing. This facilitates faster patient recruitment, site feasibility analysis, and cross-silo AI development while maintaining data privacy and security behind the firewall.
Funding
$7.1M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Clinical trials and data science projects are often hindered by the need to centralize sensitive data, creating privacy risks and logistical challenges for data owners. Collaboration between data owners and algorithm developers is further complicated by governance and IT control requirements such as GDPR and HIPAA.
Solution
Bitfount is a federated AI and data science platform that enables secure collaboration on sensitive data without requiring data centralization. The platform connects data owners, algorithm developers, and problem solvers, allowing them to train, run, and evaluate AI models and perform advanced analytics in a privacy-preserving manner. Bitfount's desktop application and Python SDK can be installed on-premise or in a cloud environment, connecting to structured, unstructured, tabular, and imaging-based datasets. Data remains behind the user's firewall, ensuring compliance with data governance policies and maintaining data sovereignty. The platform supports a zero-trust approach with granular access controls, end-to-end encryption, and no open incoming ports in the firewall.
Target Audience
Bitfount targets data scientists, AI developers, clinical researchers, and data protection officers in industries such as life sciences, healthcare, finance, and academia who require secure and privacy-preserving data collaboration.
Features
- Federated learning and analytics without data centralization
- No-code desktop app and Python SDK for flexible deployment
- Compatibility with structured, unstructured, tabular, and imaging datasets
- Integration with Hugging Face for access to open-source AI models
- Built-in loggers for Weights & Biases, MLFlow, and TensorBoard
- Multi-layer privacy protection and governance controls, including GDPR and HIPAA compliance support
- Zero-trust architecture with granular access controls and end-to-end encryption
- Support for VPN installation for additional security