We need a world where AI could finally reach its peak-value by training on non-public data, beyond the diminishing returns of publicly available data, all while maintaining the secrecy of prompts and models. Where, after breaking into a system, hackers could only see encrypted data. Where any organization could fully migrate to the public cloud without ever exposing its data, and be fully compliant. A world where personal data can be processed without invading anyone's privacy. This world of absolute privacy is still out of reach; while we have generalized encryption at rest and in transit, we are still missing scalable encryption for data in use, leaving critical security gaps which plague digital progress. Fixing them could unlock unimaginable value for individuals and organizations. No technology other than Fully Homomorphic Encryption (FHE) can help create this world, if only it could scale. So Ravel has decided to take a radical step to solve FHE's inefficiencies at its core and invented a new scheme that the scientific community deemed impossible to construct: GRAFHEN - a highly scalable, noisefree, fully homomorphic encryption scheme. Now that the noise barrier has been broken, we believe that GRAFHEN lays the foundations for the ubiquity of highly secure and encrypted data processing, so that innovative technologies are not feared or pushed back, but widely adopted in any environment and fully embraced. Contact: inquiries@raveltech.io