Safely collaborate on distributed data

Governed, private and secure federation. Enterprise ready.

Data is often siloed and inaccessible

Data that's needed for Analytics and ML isn't always where you want it to be. It can often be behind a boundary: geographical, regulatory, organizational, or just too sensitive or costly to move.

Alternative solutions such as synthetic data, encryption, or data clean rooms can limit the validity of the result, risk data breaches, or simply don't scale.

This prevents people from innovating, collaborating and gaining valuable insights from data.

Compliantly connect distributed data for federated ML and analytics

We send the computation to the data rather than the other way round.

We leverage federation to allow you to build models over the entire data cohort, without moving a single byte.

Governance, security and privacy forms the backbone of our solution - compliance with regulation is baked in from the start.

This allows you to safely build better ML models, gain deeper insights - faster and at scale.

Connect data across different collaborative setups

Internal data collaboration

Multi-party collaboration

Data partnerships

Build your own data ecosystem

Compliance is a top priority for us

Trusted by our customers & partners

What customers are saying about Apheris


“Apheris' technology empowered granular access to European Union data for our real-world evidence study exploring treatment and outcomes in oncology. The potential to impact the understanding, creation, and adoption of therapies that cater for more diverse populations is remarkable.”

Kevin Pollock Ph.D., MPH
Director RWE Strategy, International Markets at Bristol Myers Squibb

Powering the AISB Consortium to Revolutionize AI Drug Discovery

Apheris provides the tech layer for the Artificial Intelligence Structural Biology (AISB) Consortium, an unprecedented collaboration founded by AbbVie and Johnson & Johnson aimed at transforming AI drug discovery. State-of-the-art AI models will be trained and evaluated on unique data from multiple biopharma companies without exposing proprietary information.

Want to collaborate on distributed data?

Learn how to foster collaboration in a secure, private and compliant way. Talk to us about governed data access for analytics and ML.