Rhino Federated Computing Platform

The data that would benefit your company most is often data you cannot access. Regulatory compliance, data sovereignty obligations, and other requirements make moving sensitive data impractical or prohibited. In these cases, the standard approach to building AI and analytics infrastructure on top of centralized data does not work.

The Rhino Federated Computing Platform (Rhino FCP) inverts that model: instead of pulling data to compute, the AI runs its workloads where the data lives, with no raw data ever leaving its source. Engineering and data science teams get a single control plane to orchestrate and govern AI training, inference, agentic, and analytics workloads across AWS, Azure, Google Cloud, Oracle Cloud Infrastructure, and on-premises environments while sensitive data stays exactly where it is. Installation takes less than ten minutes; data harmonization projects that previously took months can be completed in days.

PRIVACY-ENHANCING BY DESIGN Beyond the standard controls for encryption at rest and in transit, the Rhino FCP layers multiple additional protections for data in-use. Data never leaves its source environment. Confidential computing support can provide hardware-isolated secure enclaves with full attestation and encrypted memory. Differential privacy and k-anonymization prevent inference attacks on model outputs. Workloads that require regulatory defensibility get a layered architecture rather than a single control.

BUILT FOR DEVOPS AND MLOPS The Rhino FCP handles code containerization automatically, with no Docker expertise required, drastically reducing the support surface area for your team. You can monitor distributed training runs in real time with TensorBoard integration, stream live logs from remote code execution, and manage experiments across sites with built-in hyperparameter tuning and model versioning. Our Python SDK, REST API, and MCP server support full programmatic control.

MULTI-CLOUD COMPUTE ORCHESTRATION With the power of our federated computing platform, you are able to provision and manage compute resources, including GPU infrastructure, across AWS, Azure, Google Cloud, Oracle Cloud, and on-premises from a centralized dashboard. Usage minimums, elastic compute, and VM pooling put cost management in your hands. This multi-cloud support enables your organization to run federated learning, distributed training, and heavy analytics workloads across heterogeneous environments.

ANALYTICS AND INFERENCE ACROSS DISTRIBUTED DATA The Rhino FCP enables statistical analysis, explainability tools, bias and fairness assessments, and batch inference across distributed data sources, all without the underlying data leaving its location. Built-in support for regression, model evaluation metrics, survival analysis, epidemiological studies, and other statistical suites via native Python support means modeling teams spend less time building infrastructure and more time on evaluation and science.

UNIFIED GOVERNANCE ACROSS EVERY ENVIRONMENT Rhino provides bi-lateral role-based access controls (RBAC), audit trails, and security policies across all participating nodes from a single control plane, supporting compliance with HIPAA, GDPR, ISO, and internal requirements without bespoke configuration per environment.