Sovereign AI & Local Models | Anexia Digital Engineering

Our offering is aimed at companies with particularly sensitive data and requirements regarding the place of processing. For companies in Germany, Austria and Switzerland, we plan the Private LLM based on the existing systems and the collaboration required.

For private model operations, we describe which data may leave the controlled environment and which functions must run entirely within the chosen infrastructure. Model operations, document ingestion, vectorisation, logging and administration are considered separately. A locally operated model is not enough if other components pass sensitive content to external services. The architecture decision therefore follows the complete processing path.

For internal AI operations, we then check whether available models can fulfil the business task with the planned resources. Model size, context length and parallelism affect memory requirements, response time and operating costs. A comparison is made using representative inputs and the intended load profile. For Private AI, updates, security patches and recovery are also planned. Taking on operational responsibility yourself involves more than starting a model once. A suitable scope of services therefore combines proof of functionality with operational readiness. If a private environment cannot meet individual requirements economically, we document this limit early. As a result, the Private LLM is aligned with specific protection and performance requirements instead of being described solely by the location of a server.

Scope of services for Private AI

The agreed scope for private model operations can include the following deliverables: model selection, infrastructure profile, data flow review, update procedure, access model and recovery concept. For each key deliverable, we define what it is for, who reviews it and in what form it is handed over. This allows development, operations and procurement to assess the same service. Vague umbrella terms in the proposal are replaced by specific work packages.

Internal AI operations also include agreeing the necessary interfaces with your team. We clarify access, contact persons and available testing options before the relevant implementation steps. Changes to the scope are described together with their impact on effort and sequence. This gives you a clear overview of delivered results, outstanding prerequisites and additional requests.

The key technical decision for Private AI

Dedicated operation, shared private infrastructure and local installation are compared based on load profile, need for control and support effort. For the Private LLM, we document the relevant assumptions and explain the reasons for the chosen option. Key alternatives are considered against the same business requirements. This makes later adjustments easier if load, data volume or organisational responsibility change.

A sound decision also takes maintainability and handover into account. For private model operations, we check which skills are available internally and what additional support is needed. Technical possibilities are thus linked to a realistic operating model. The client can see which tasks will recur after the project and which decisions remain with them.

Your own server prevents neither unauthorised internal access nor data leakage via logs and connected tools. In internal AI operations, we treat this point as a specific review question. Each risk is linked to the affected process, its possible impact and a suitable measure. Where prerequisites are still missing, the statement is explicitly left open until the necessary investigation has been carried out.

We check memory requirements, parallelism, response times, residual data, updatability and behaviour under capacity bottlenecks. For Private AI, we record the starting situation, the tested configuration and the result. Acceptance refers to the agreed scope. Open limitations are documented visibly and assigned to an owned next step. This allows your team to distinguish between proven functionality, a deliberate decision and an assumption that has not yet been confirmed.

Project example as an illustrative scenario

The following scenario is an application example and not a customer reference. A company wants to make confidential development documents accessible through an internal assistant. For Private AI, the entire data path would be captured first: inputs, source documents, model calls, logs and backups. The decision for private operation would only be meaningful once maintenance access, updates and supporting services have also been considered.

For the Private LLM, a representative workflow would be tested on the intended infrastructure. Response quality, concurrent use, queues and resource requirements would be measured together. A model that fits on one accelerator would not automatically be suitable for the expected response times. The pilot would also include restarts, model updates and the recovery of required data. Administrator rights and the use of logs would be governed separately from business access to documents.

The decision on private model operations would include a comparison of the need for control, operating effort and usable quality. Planned deliverables would be a data flow model, a tested operating option and clear responsibilities for updates and incident handling. An expansion would only take place once capacity and support for additional users have been clarified. The private location alone would imply neither complete security nor functional correctness; both properties would have to be examined for the specific application.

Our experience and our engineering approach

We combine custom software development with the technical tasks of introduction and further development. For the Private LLM, we bring the perspectives of application, integration and operations into a shared workflow. Business requirements remain linked to technical decisions. You work with a defined scope of services and traceable results.

Our experience should be verifiable for your specific project. That is why, in the technical discussion, we explain how we examine the relevant risks, which deliverables are planned and how your team can assess them. For private model operations, we match the roles involved to the actual need. Available references are presented with their respective project and company context; confidential customer details are not used as general marketing claims.

Effort for private model operations

Hardware utilisation, model size, operational readiness and maintenance can result in higher unit costs than an external service when usage is low. We therefore calculate Private AI on the basis of a clearly defined scope and explicitly stated prerequisites. An initial analysis step can make sense if key technical information is still missing. Work packages and acceptance criteria can then be agreed more reliably.

The Private LLM can be organised as a limited project, as a joint implementation with your team or with subsequent support. Rights of use, access, documentation and responsibilities are clarified in the contract. Ongoing operating hours, response times and external consumption costs are subject to separate agreements. This makes it clear which services are provided once and which tasks recur after the introduction.

What Private AI does not cover

Private deployment is not blanket proof of data protection compliance or functional correctness. For internal AI operations, we state this limit in the proposal itself. Any additional services required are included with their business rationale and are not silently assumed. This allows your team to plan suitable internal skills and divide the overall work sensibly among those involved.