Our early efforts resulted in foundational intellectual property, peer-reviewed publications, a reputation for technical excellence, and a selection of patents.

As demand for privacy-preserving data collaboration and secure AI has grown, we’ve evolved our focus from research toward practical validation and application. Today, we’re focused on translating our work in MPC and other privacy-enhancing technologies into approaches that can address real-world problems, including secure machine-learning.

Our strategy remains grounded in research, with a focus on understanding where privacy-enhancing technologies can solve meaningful real-world problems. We work to bridge the gap between technical research and practical use, particularly where sensitive data, collaboration, and privacy requirements make traditional approaches difficult.