Rapha Protocol vs Owkin

1. Access to Way More Real Clinical Datasets — Instantly

Rapha Protocol connects you to real hospital imaging, EHR, and clinical text data — orders of magnitude more datasets than any competitor. Owkin's approach requires per-hospital contracts, per-trust legal review, and per-site technical integration. Each hospital takes 6-18 months to onboard. Each additional hospital requires the same cycle again.

Rapha Protocol: hospitals deploy one edge appliance. All AI companies connect through the same infrastructure. One hospital onboarding unlocks datasets for every AI researcher on the network. Owkin's model is linear — your data access grows one painful contract at a time. Rapha's model is networked — each new hospital multiplies the available datasets for every existing user.

And because Owkin requires multi-site pharma consortium coordination, the legal overhead scales with the number of hospitals. Each new site adds another contract layer. Rapha trains at one hospital at a time — one agreement, immediate access.

2. Access the Entire Network in 3 Commands — Not 18 Months of Procurement

Owkin's timeline: initial contact → legal review → data sharing agreement → DPIA → compliance assessment → technical integration → pilot → production. Typical outcome: 12-18 months, $200K+ in legal fees, and the project may still be rejected by the hospital's DPO or Caldicott Guardian.

Rapha Protocol's timeline:

Three commands. Your model trains on real clinical data inside the hospital's SGX/TDX enclave. You receive trained weights, not patient data. The entire procurement cycle — legal, compliance, technical, financial — is handled by the Rapha Protocol infrastructure layer, not by your team.

With Owkin, every one of these steps is your problem. With Rapha, they are solved before you send your first command.

3. Infinitely Easier to Use — Designed to Just Work

Owkin requires you to understand federated learning architectures, gradient aggregation, non-IID convergence issues, and multi-site coordination.

Rapha Protocol is designed so an AI researcher who has never touched healthcare infrastructure can submit a training job in under 5 minutes. The SDK handles authentication, job submission, status polling, and artifact download. The secure API manages ZK-TLS sessions, developer keys, and proof verification server-side. You focus on your model. We handle the healthcare complexity.

• No hospital contracts to negotiate. No DUAs to draft. No DPIA to write.
• No cloud infrastructure to provision. No GPUs to rent. No networking to configure.
• No FL coordination. No gradient leakage mitigation. No non-IID convergence debugging.
• One SDK. Three commands. Trained model weights as output.

4. Military-Grade Security — So Your Company Does Not Get Sued Into Oblivion

Healthcare AI companies face existential legal risk. One PHI breach — one misconfigured S3 bucket, one compromised API key, one de-identified dataset that gets re-identified — and your company faces regulatory fines, civil lawsuits, criminal liability, and reputational destruction. Owkin's model: gradients leave the hospital. Gradient inversion attacks are published and reproducible. Your legal exposure is real.

Rapha Protocol's security architecture eliminates PHI liability:

• Data never leaves the hospital. Not encrypted. Not de-identified. Not "pseudonymised." Never exported. Period. No PHI custody = no PHI breach liability.
• SGX/TDX hardware enclave. Intel CPU-level memory encryption. Even the hospital's own IT staff with root access cannot inspect data during training.
• Rust kernel air-gap. The WAN interface is physically severed at the kernel level during training. Data cannot be exfiltrated because there is no network path.
• Go OPA compliance guard. Every training script and Docker container is statically analysed for network-capable dependencies before execution. socket, requests, urllib, http.client — all blocked.
• Output validation. Only approved file types (.safetensors, .pt, .json) can leave the enclave. Raw data files (.csv, .dcm, .jsonl, .fhir, .parquet) are blocked at the filesystem level.
• On-chain proof receipts. Every training job produces a cryptographic proof receipt anchored on Polygon mainnet. You can prove — to regulators, to auditors, to your board — that no PHI was exported.

The bottom line: Owkin's architecture creates PHI exposure. Rapha Protocol's architecture eliminates it. When the regulator asks for your PHI exposure assessment, which answer would you rather give?