The most useful AI workflows often need business context. That does not mean every document, message, or decision record should be copied into a provider-controlled service. We build private AI workspaces where teams and approved agents can work with governed data inside infrastructure the organization controls.

That can mean controlled collaboration, single sign-on, and local or in-jurisdiction inference for high-sensitivity data when the client selects that deployment model. The architecture is designed from the start to support data sovereignty and GDPR obligations, not retrofitted after an audit.

When the requirement is broader than an AI workspace, the same operating model can become a Private Business Cloud: a ProBiz Sovereign Cloud deployment with controlled identity, collaboration, automation, backups, observability, and recovery around the business systems that need stronger ownership.

Residency and sovereignty are not the same

Data residency answers where information is stored. Data sovereignty asks a broader set of questions: who operates the systems, who holds the encryption keys, who can authorize access, what other jurisdictions may apply to the provider, and whether the organization can move or recover the environment without depending on that provider.

Stronger control can mean using local hardware, an in-jurisdiction provider, self-hosted open-source software, local inference, or a deliberate combination. The right answer depends on the data, the threat model, the business's operating capacity, and the obligations that actually apply.

Sovereignty is a partnership. When a client chooses in-jurisdiction infrastructure and the required operating model, we can design the workspace to keep governed data inside that boundary:

Infrastructure you control: compute, storage, and backups placed where the business's requirements and operating model justify them.
Keys and access you govern: encryption and access decisions designed so control is not limited to a provider account.
Local inference where it matters: selected AI workloads run close to the governed data instead of sending that context to an external model service.
An operating responsibility: monitoring, patching, backup verification, restore testing, and documentation are part of sovereignty, not optional extras.

Not every workload needs this model. We help decide where the additional control is worth the responsibility and where a well-chosen external service remains the better business decision.

What you get

Self-hosted collaboration environment
Single sign-on and granular access control
Local or in-jurisdiction inference for sensitive data when the client selects that deployment model
AI agents integrated into the workspace alongside your team
Architecture designed to support data sovereignty and GDPR obligations across data at rest, in transit, and in backups
Migration from existing tools and ongoing support

How it works

1. Architecture

We map your data boundaries and applicable requirements: what stays inside controlled infrastructure, what can use an external service, and why. You get a clear picture of how governed data is stored and routed before anything is built.

2. Build

We stand up the workspace: collaboration, access control, the selected inference model, and the agent integrations that make it AI-native.

3. Migrate

We move selected workflows and data in planned stages, with validation and a rollback path before wider adoption.

4. Operate

Ongoing support, hardening, and iteration as your needs evolve.

Who it's for

For organizations handling sensitive or regulated data, including professional services, healthcare, finance, and EU-based teams, that need stronger control over where AI work runs and how governed data is accessed.

AI-Native Workspaces