Objective 01
NN-based opaque encryption runtime
The serving pipeline is designed so encryption structure and inference-time transformations remain hidden. Practical reverse engineering of the weight path is blocked by design.
Encrypted Inference Engine
Encrypted inference. Zero weight exposure.
Tooliense serves proprietary neural networks through a hardened encrypted runtime. The encryption path remains opaque, so model weights are not practically accessible by any method.
Objectives
EIE is implemented with neural network complexity as a protective layer. Attackers cannot infer encryption internals or recover model weights, even when runtime behavior is observed.
Objective 02
Hard boundary between model IP and operations
Operators can run and scale workloads, while the valuable model internals stay inaccessible. This separates service reliability from model intellectual property exposure.
Security Confidence
Our philosophy is simple: model ownership is non-negotiable. Built through focused encrypted-inference engineering, EIE keeps serving practical while keeping weights out of practical reach.
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Tools + Sapiens = Tooliense. Humans evolved through collaborative tool use and shared knowledge. As Sapiens learned to protect one another, our EIE protects the models they create.