Define your boundaries
Agree where data, models, logs and backups live. Map administrative access and external dependencies before selecting the architecture.
PRIVATE BY DESIGN
Use AI without moving sensitive data outside your environment.
For enterprise and government IT leaders who need AI while keeping control of sensitive information. We deploy models, internal knowledge retrieval and assistants on premises or in an approved private cloud, with agreed access and operating controls.
WHAT WE BUILD TOGETHER
Build inside your environment
Deploy retrieval, inference and agent services on premises or in an approved private cloud. Connect identity, network controls and your existing systems.
Operate with confidence
Plan model evaluation, access reviews, monitoring and recovery. Sovereignty is an operating model as much as a hosting decision.
UNDERSTAND THE ENGAGEMENT
What it is.
How we deliver it.
What you are buying
Sovereign AI is an operating model for retaining defined control over data, models and their dependencies. On-premises describes location; private describes access or isolation. We turn your sovereignty requirements into explicit architecture and operating decisions instead of relying on a hosting label.
Where it is useful
A useful starting project is an internal assistant for approved procedures or enterprise knowledge. This suits teams that need access to AI capabilities while controlling sensitive documents, administrative access and external dependencies. We first establish which restrictions are mandatory and which are preferences.
What a delivery scope can include
A proposed scope covers the data-flow map, selected models and licences, inference services, retrieval, identity, network boundaries, logging and recovery. We identify where documents, extracted text, embeddings, conversation records and backups live. Third-party connectors and support access are included in the boundary review.
What to bring to discovery
- Required data and administrative boundaries
- Sample workflows and approved source material
- Identity groups, retention and recovery requirements
- Existing infrastructure and operating-team responsibilities
How we evaluate the result
Agree acceptance around representative user tasks, restricted-access tests, unsupported-answer behaviour and a recovery exercise. Model updates and source changes need a repeatable evaluation process. The handover should identify owners for patches, access reviews, incidents and capacity.
Decisions to make early
Private deployment creates operating obligations. Hardware, support coverage and availability targets are scoped explicitly. A sovereign architecture does not itself establish legal or sector compliance; the responsible governance teams must approve the requirements and controls.
BUILD YOUR UNDERSTANDING
Useful reading before we talk.
Private AI & RAG · 3 MIN
Explore all services ↗Sovereign AI vs private AI vs on-premises AI
Separate data location from operational control and turn sovereignty requirements into an architecture checklist.
AI for business & the GCC · 3 MINAI in the UAE and GCC: developments that matter for business planning
A dated perspective on government AI programmes, private deployment, agents and the practical questions enterprises should ask.
A LITTLE MORE CLARITY
Good questions.
Clear answers.
Can everything stay on premises?+
An on-premises architecture can keep supported workloads and data inside your environment. We first review model licensing, hardware, integrations and any services that require external connectivity.
Is sovereign AI automatically compliant?+
No. Applicable UAE and sector requirements need to be assessed against the actual architecture and operating procedures. We work with your security and governance teams to define the controls.
DISCUSS YOUR FIRST USE CASE