Sovereign VPC deployment for regulated AI workloads
How Intelligence Fabric runs entirely inside your network when your regulator, customer, or geography does not accept a shared cloud.
The problem
The data cannot leave the country. The model cannot leave the VPC. The audit log cannot leave the accredited boundary. The vendor says "we're cloud-native, we don't do on-prem." The program team has a signed contract they cannot deliver against. Everyone blames procurement for not catching it earlier.
This is the sovereign-deployment gap. It is not a rare edge case. It is the default for German banks, Swiss insurers, GCC-region regulators, federal US programs, and a growing share of healthcare systems in every region that has a data protection authority with teeth.
Why the usual approach breaks
Multi-tenant SaaS platforms assume a shared control plane. Making them run in a customer VPC usually means forking the product, losing upgrade path, and inheriting a year of customer-specific maintenance debt. Most AI vendors do not do this seriously, they offer a "dedicated tenancy" that is still inside the vendor's cloud and still shares a trust boundary the customer's regulator will not accept.
The customer ends up rebuilding primitives internally: prompt templating, observability, RBAC, audit, retrieval. Each primitive is a distraction from the product the customer actually wants to ship.
How Intelligence Fabric closes the gap
Fabric is designed to run inside a customer-controlled network as a first-class deployment mode, not an afterthought. The platform runs on the customer's Kubernetes. Model endpoints route exclusively to customer-approved providers, whether that is a hyperscaler's in-region LLM service, a sovereign cloud, or a self-hosted model. Observability data stays inside the boundary. Upgrades deliver as signed artifacts the customer's platform team pulls on its own cadence.
The customer gets the full platform surface: orchestration, retrieval, policy, audit. The vendor gets no trust-boundary crossing it cannot defend to the customer's regulator. The relationship is software delivery, not data custody.
Implementation pattern
The customer's platform team provisions the required infrastructure: a Kubernetes cluster, a postgres, a blob store, a vector database of the customer's choice, and network egress rules that allow only the model endpoints the customer has approved. Nuviax provides the helm charts, the upgrade tooling, and the operational runbooks. The customer's SRE team runs the platform, Nuviax provides enterprise support on the customer's terms.
When the regulator asks where the data is, the answer is specific and verifiable.
Next step
An architecture review maps your data residency, network isolation, and upgrade cadence requirements into a deployment plan your platform team can execute against.
Next step
Map Intelligence Fabric against your stack in 90 minutes.