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README.md

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 ## How Nesa Achieves Blind AI: Equivariant Encryption (EE) ##
 
 At Nesa, privacy is a critical objective. On our path toward universal private AI, we confronted a key challenge: **how can we perform inference on neural networks without exposing the underlying input and output data to external parties, while returning requests without high latency?** Traditional approaches, such as differential privacy, ZKML or homomorphic encryption (HE), while conceptually strong, fall short in practical deployments for complex neural architectures. These methods struggle to handle non-linear operations efficiently, often imposing substantial computational overhead that makes them infeasible to integrate into real-time or large-scale systems.