Opaque Systems provides a confidential AI platform that enables organizations to run AI workloads and analytics on encrypted sensitive data. The platform was developed by researchers from UC Berkeley's RISELab and built on the open-source MC2 (Multiparty Collaboration and Competition) foundation. The company's technology uses hardware-level privacy protections and confidential computing to allow multiple parties to perform joint analytics and machine learning model training on collective data without revealing individual datasets to each other. The platform supports popular programming languages and frameworks including Python and Apache Spark, enabling cloud-scale AI workloads while maintaining data encryption during processing. Opaque implements zero-trust architecture, quantum-resistant privacy safeguards, and cryptographically verifiable audit capabilities. The solution enforces data sovereignty requirements and provides automated regulatory compliance features. Opaque serves multiple industries including financial services, technology companies, government agencies, and research organizations. Use cases include secure transaction processing, collaborative AI model development, cross-border data collaboration, and privacy-preserved AI inference. The platform addresses challenges where traditional data masking and privacy tools cannot maintain both data protection and AI model accuracy simultaneously. The company was founded in 2021 by Professor Ion Stoica, Professor Raluca Ada Popa, Dr. Rishabh Poddar, Professor Wenting Zheng, and Chester Leung. The founding team includes researchers who previously contributed to projects like Databricks and Apache Spark.