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Stained Glass Engine™

Protopia AI’s Stained Glass Engine™ (SGE) helps AI/ML Engineers protect their sensitive data for AI workloads by creating Stained Glass Transforms (SGTs).

With Stained Glass Engine (SGE) , machine learning engineers can extend their existing training loops to create an SGT. Without modifying the base model code, users can wrap their existing loss functions, optimizers, and models with API calls into SGE.

Dive deeper in the SGE documentation.

Optimized Training Process

SGE utilizes PyTorch hooks to manipulate the loss function, manage data flows, and implement memory optimizations during the creation and training of SGTs, ensuring efficient performance without compromising utility

Broad Compatibility

SGE supports any PyTorch module, including Hugging Face Transformers, enabling smooth integration into virtually any training loop, allowing users to extend their AI models with SGTs effortlessly

Hyperparameter Customization

SGE offers hyperparameters that balance the strength of SGTs and the utility of the original model, providing flexible control over the stochastic transformations during the SGT creation process

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Latest News & Articles

Announcement

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The direct win is being able to serve workloads that used to require dedicated, single-tenant endpoints at multi-tenant utilization instead. In Protopia’s modeled deployment economics, that shift meaningfully lowers annual infrastructure cost for those workloads, since they’re no longer sitting in underutilized carve-outs (actual savings depend on workload mix and scale).

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Useful Agentic AI Has Arrived

Protopia AI’s SafeCLAW makes inference safe for sensitive data, so the work that was previously paying the isolation tax can route to the multi-tenant factory like everything else.

SGT for NVIDIA Nemotron 3

Sensitive Data, Open Models, and the AI Factory’s Inference Privacy Layer

Protopia AI Stained Glass Transform (SGT) is now available for NVIDIA Nemotron 3 Super and Nemotron 3 Nano Omni. As an integrated data-privacy layer, SGT is designed to minimize plaintext exposure from the inference path, so an enterprise’s most sensitive workloads can run on the multi-tenant AI factory infrastructure where supported.