Rapha Protocol is a zero-trust, compute-to-data edge network for clinical AI. Instead of exporting raw patient records and risking massive GDPR/HIPAA liabilities, Rapha routes AI models into air-gapped hardware enclaves directly inside hospital server rooms. Models train locally, and only anonymized weights leave. Key features include Intel SGX/TDX isolation, Policy-as-Code compliance enforcement, and immutable Zero-Knowledge (ZK-SNARK) audit receipts anchored on Polygon Mainnet.
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Maker
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Hey everyone! 👋
I’m the founder of Rapha Protocol. Right now, healthcare AI is facing a massive bottleneck: developers are starving for real-world clinical datasets, but moving raw patient data out of hospital firewalls is an 18-month legal and security nightmare.
We engineered Rapha to completely reverse this pipeline. Instead of moving the data to the model, we move the model to the data. By deploying secure, air-gapped edge nodes directly into clinical environments, AI teams can run parameter-efficient fine-tuning (like LoRA) inside hardware-secured Intel SGX enclaves. The raw patient data physically never touches the internet, and compliance is mathematically proven via ZK-SNARK receipts anchored on Polygon Mainnet.
We are currently prepping our 'Node 0' deployment for university translational labs. I'd love to hear your thoughts on our architecture, privacy stack, or decentralized compute model. Let’s get the discussion started!