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Sovereign AI

Sovereign AI: decide where your data lives and who stays in control.

Scope the use cases, data flows and dependencies before choosing an architecture. For organizations that want to adopt AI while keeping control of their own systems.

Scope my AI projectSee the deliverables

This work fits your situation if…

  • A clear use case, a framework still to define

    Your teams want to work with internal documents or automate a task. You need to specify which data can be used, by whom and in which environment.

  • A dependency to reassess

    An existing solution limits your hosting, integration or exit options. The task is to compare alternatives based on how things actually run, the costs to examine and the skills available.

  • A project business and IT need to share

    Business owners, IT and the people in charge of security need the same scope: expected benefit, access, limits and operational responsibilities.

What the engagement can deliver.

The scope and deliverables are agreed together during scoping.

A decision scope
Priority use cases, data involved, success criteria and exclusions. Scoping also identifies the tasks that can be handled without a generative model.
An architecture with its rationale
A map of data flows and a comparison of the relevant options: local, dedicated hosting or approved external services. Each choice sets out its dependencies and operating conditions.
Operating rules
Access rights, logging, data retention, human review and exit scenarios are defined with the people accountable on your side.
A delivery plan
Priorities, required integrations and an evaluation protocol to move from scoping to a first verifiable use case. Development is covered by a scope we agree on together.

A clear sequence.

  1. Start from the use case

    Describe a task, its users, the documents and the current constraints. An anonymized example keeps the discussion concrete.

  2. Compare the options

    Study data flows, dependencies, integration and operations. The trade-offs are set out in a document that business and technical teams can both review.

  3. Decide what comes next

    Select a testable scope and its acceptance criteria, or conclude that another approach fits the need better.

Case studies that show the work.

Sovereign queue management

A business application built to regain control from a proprietary tool. This work illustrates how dependencies and operations are handled; it is not presented as an LLM deployment.

Read the case study : Sovereign queue management

Before we start.

Do we have to host everything on our own servers?

That is a choice to examine, not a starting point imposed in advance. Data location, authorized access, acceptable dependencies and your capacity to operate the system shape the architecture. Scoping makes these trade-offs explicit.

Can we start from a system already in place?

Yes. The work can start from an existing tool, its contracts, its technical diagram and a user journey. The first goal is to identify what needs to change and what can be kept.

Does scoping include development?

Scoping and delivery are two separate scopes. We define the expected deliverables together before starting. If delivery follows, it builds on the decisions and acceptance criteria already established.

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Next step

Let’s start from your need.

Describe the intended use, the data involved and the constraint holding you back. A first conversation helps determine what needs scoping.

Scope my AI project
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