— Private artificial intelligence
Deploy a private model server securely
Use generative artificial intelligence without sending sensitive data to a public platform. I plan and implement a private model server on your own infrastructure or in a location you control.
From selecting an appropriate open model through access protection, measurement, and handover, the work produces a traceable path into operation. Your data stays on customer-owned or customer-controlled infrastructure.
— Deployment options
Deployment options
On your own infrastructure
The model server runs in your data centre or at your site. You control storage, network access, logging, and lifecycle.
With a provider you choose
The service runs in an environment you control in Germany or the European Union. Accounts, keys, and data access remain under your authority.
A small pilot before expansion
A limited use case tests model quality, response time, and resource needs before you invest in larger infrastructure.
— From idea to operation
From idea to operation
1. Clarify requirements and data flows
We identify users, data classes, intended tasks, quality targets, and boundaries. These become a testable target architecture.
2. Test models and infrastructure
Several suitable open models are compared on representative tasks. Performance, memory demand, quality, and operating cost become measurable.
3. Deploy securely and integrate
I configure the server with clear access boundaries and connect it carefully to your documents or tools.
4. Verify and hand over
Acceptance tests, operating documentation, monitoring, and a fallback path make the solution maintainable by your team.
— Security and control
Security and control
Data remains under your control
Prompts, documents, and outputs do not leave the infrastructure you define. External connections are introduced only deliberately and documented.
Access follows the task
Users and services receive only the permissions they need. Authentication, network boundaries, and logging are checked before release.
Traceable changes
Models, configuration, and tests are versioned. Updates can be assessed in a separate environment first and rolled back if needed.
— What you receive
What you receive
A defensible decision
You know which model and infrastructure meet your use case and where the limitations are.
A working deployment
The model server is reachable, protected, and connected to the agreed workflows.
Measurable acceptance
Defined test cases evaluate quality, response time, and failure behaviour instead of relying on isolated demonstrations.
Documented operations
Your team receives practical instructions for startup, monitoring, updates, backup, and fallback.
— Frequently asked questions
Frequently asked questions
Do we need our own data centre?
Which model is right for us?
Does our data really stay with us?
Can the model server work with internal documents?
What happens after deployment?
Discuss your private model server
Briefly describe the use case, existing infrastructure, and your privacy and operating requirements. I will reply with an initial assessment.