about us

About Zosher AI

Zosher platform provides the best-of-breed approach to a safe, rapid, and managed implementation at scale to use interested platforms for federated & privacy-preserving data science. The core capabilities of Zosher platform provide an operational blueprint for scalable, secure data collaborations with multiple partners, while providing flexibility by supporting already-existing tech stacks, pipelines, data lakes, and data products. Zosher provides the specifics in your context on how to implement Federated MLOps into enterprises and include tons of valuable insights for your next step in unlocking the true value of your data in a privacy preserving setting.

With Zosher, data scientists can securely and intuitively build, deploy, and operate privacy-preserving AI applications across organizational boundaries. Unlock the capability for your organization to enter secure data collaborations in an ethically governable and standardized way – if you are interested in exploring how Zosher enables your collaborative data ecosystem, get in touch. We would love to chat!

Experience a new age of privacy tech

Zosher’s privacy technology helps you move beyond the limited traditional privacy techniques, such as encryption schemes that secure information in transit and at rest, and de-identification techniques such as tokenization and k-anonymity to provide more comprehensive solutions to privacy challenges in modern data-driven systems in your context.

Our Approach

Zosher aims to establish a data ecosystem, whereby data is shared and made available in a trustworthy environment. It is designed to give control back to the users by retaining digital and data sovereignty.

Our Mission

Together we create an open, transparent, and secure privacy-preserved digital ecosystem, where data and services respond to common human-centrist rules and can be freely and securely built, collated, and shared.

Our Vision

Digital platforms are becoming the digital twin of economic, political, and societal ecosystems. Our ability to ensure their respect for fundamental principles of freedom, transparency, and sovereignty, will determine the future of our society.

Roy Saurabh

Roy Saurabh



Roy has vast interdisciplinary research and applied experience in AI, privacy policy research and data governance architectures in tackling grand challenges leveraging ethical ML. Roy is a HCI researcher & has architect-ed affect-aware digital platforms to analyze and model learner’s cognitive & affective trajectory while building mental health in privacy-preserving architecture.

Sarah Goodday

Sarah Goodday

Scientific Advisor

Sarah (Ph.D.) is a social and psychiatric epidemiologist whose work centers on improving methodological approaches for the detection of stress and disease manifestations using digital devices to inform new phenotypes and ways of understanding the progression of chronic conditions. She has vast experience in handling sensitive data in compliant settings.

Harri Ketamo

Harri Ketamo

Scientific Advisor

Harri Ketamo (Ph.D.) is an entrepreneur with 20 years of experience in cognitive sciences, computational intelligence, complex adaptive systems, game development and General Semantic AI for transparent decision making. Harri actively participates in academic research as a senior fellow at University of Turku and at Satakunta University of Applied Sciences.


Provide end-to-end security, verifiability and governance techniques that ensure access and sharing of sensitive data in the context of critical information flows.



150-154, Rue du Faubourg, Saint Martin
Paris, FR 75010