Bridge the gap between research and patient care. Jozu provides the secure, OCI-native MLOps foundation to deploy life-saving algorithms across the Mayo Clinic network 7x faster, without ever compromising patient privacy.
Built on Open Enterprise Standards
Jozu bridges the gap between Mayo Clinic's data science innovation and rigorous hospital IT requirements.
Keep your data and models where they belong. Jozu’s on-premise architecture ensures strict adherence to HIPAA. Process PHI locally without relying on public AI hubs.
Simplify FDA (SaMD) and NIST AI RMF compliance. Jozu secures the supply chain with tamper-proof ModelKits, automated CVE scanning, and cryptographic provenance.
Stop rebuilding infrastructure for every new algorithm. Jozu integrates natively with your existing Kubernetes clusters and OCI registries (like Artifactory).
Mayo Clinic cannot risk downloading unverified models from public hubs. Jozu acts as your secure, internal clearinghouse. Every model is packaged immutably, scanned for vulnerabilities (CVEs, toxic prompts, data poisoning), and cryptographically signed before it is allowed into your clinical environment.
Integrating fragmented AI tools into Mayo's enterprise IT is a nightmare. Jozu is fully OCI-compliant. ModelKits live in your existing container registries (like JFrog Artifactory) and deploy to your existing Kubernetes clusters using the standard GitOps workflows your team already uses.
Your researchers build brilliant models, but IT blocks deployment due to missing dependencies or security concerns. With a simple `kit pack` command, your team packages weights, clinical data, and code into one standardized artifact. Handoffs to DevOps become instant and error-free.