Greyhaven provides a sovereign AI platform that runs entirely within a client’s own infrastructure, keeping data, models, and inference processes on‑premise or in a controlled VPC. The solution offers flexible model selection, fine‑grained access controls, sandboxed execution, and audit‑ready observability, enabling regulated enterprises and technical teams to integrate private, compliant AI tooling into their existing workflows.
Funding
Funding not disclosed
Founders
Product
Problem
Enterprises and regulated organizations often rely on external AI services that require sending sensitive data to third‑party providers, limiting control over model selection, data exposure, and auditability. This creates compliance risks, vendor lock‑in, and a lack of transparency into AI decision‑making.
Solution
Greyhaven delivers a sovereign AI platform that runs inside a client’s own infrastructure, keeping all data, models, and inference processes on‑premise or within a controlled VPC. The platform lets organizations choose and switch between AI models, decide where each model is hosted, and enforce fine‑grained access controls and sandboxing at the network level. Built on the Monadical foundation, it provides built‑in observability and audit trails that record what data was used, how models behaved, and the outcomes of each request. By embedding engineers with client teams, Greyhaven tailors the solution to existing workflows, ensuring the AI tooling aligns with real operational needs while maintaining security and compliance.
Target Audience
Primary customers are SME teams, public‑sector and regulated entities, research and technical organizations, and infrastructure/service providers that require private, auditable AI tooling integrated with their existing operations.
Features
- Self‑hosted AI runtime that runs within the client’s environment, eliminating data exfiltration to external providers
- Flexible model selection and routing, allowing different tasks to use the most appropriate model based on capability, cost, and data sensitivity
- Configurable inference locations (on‑device, VPC, or cloud) with per‑workload decisions and easy portability between providers
- Network‑level data exposure controls, including PII stripping, egress policies, and classification‑based routing
- Sandboxed execution environments that limit filesystem, network, and tool access for each AI workload
- Role‑based permission system enforcing least‑privilege access for AI agents acting on behalf of users
- Comprehensive observability suite with audit‑ready logs, request tracing, and model evaluation metrics tailored to client use cases
- Open‑source foundations for transparency and long‑term flexibility, built atop a decade of secure, production‑grade engineering experience