Apheris provides a federated learning platform that enables secure and compliant collaboration on distributed data without the need to transfer sensitive information. This technology allows organizations to build machine learning models and gain insights from diverse data sources while maintaining data privacy and regulatory compliance.
Funding
$41.5M 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
Organizations often struggle to access and utilize distributed data for analytics and machine learning due to geographical, regulatory, organizational, or sensitivity boundaries. Traditional solutions like synthetic data, encryption, or data clean rooms can compromise result validity, risk data breaches, or lack scalability.
Solution
Apheris offers a federated learning platform that enables secure and compliant collaboration on distributed data without requiring data transfer. The platform sends the computation to the data, allowing organizations to build models over an entire data cohort while maintaining data privacy and regulatory compliance. This approach facilitates building better machine learning models and gaining deeper insights faster and at scale.
Target Audience
The primary customers are enterprises in industries such as pharmaceuticals and biotechnology that require secure and compliant data collaboration for machine learning and analytics.
Features
- Federated machine learning and analytics across distributed data sources
- Secure computation without data transfer
- Governance, security, and privacy baked into the solution
- Compliant with regulations from the start
- Compute Gateway to connect data across different collaborative setups
- Supports internal data collaboration, multi-party collaboration, and data partnerships
- Enables building custom data ecosystems