Cloud & on-prem
Your AI systems deployed wherever your constraints require, down to your own infrastructure. The agent acts on your tools, and when the model and the MCP server run inside your perimeter, no data ever leaves.
Sovereignty isn't optional for everyone. I deploy on your cloud when speed matters most, and directly inside your infrastructure when your data must never leave. Private open-source models, EU hosting: you decide where the boundary sits.
Key facts
on-prem
models and MCP server inside your infrastructure
EU
sovereign hosting, outside the US Cloud Act
0
data leaves your perimeter, by design
What I build
Sovereign MCP server
Your CRM, emails, databases and tools connected to the agent through an MCP server that runs in-house or on an EU cloud. Paired with a private or EU-hosted model, the agent reads, decides and acts on site: neither your documents nor your queries go to a third party. I work out with you where the model needs to run so the promise actually holds.
MCP · On-prem · Sovereignty · EU cloud
On-prem & private models
Open-source models deployed inside your infrastructure: your data never leaves your perimeter, not to answer, not to act, not to train anything.
On-prem · Open-source · Private models · Security
EU deployment & industrialization
Scalable, monitored production deployment on an EU cloud or your own cloud (AWS, GCP, Azure, Vercel), with CI/CD, versioning and zero-downtime updates. I check where your models actually run, not just the logo on the invoice.
EU cloud · CI/CD · MLOps · Zero-downtime
The promise
Your data stays with you, even when the agent acts.
Before / after
AI that has to send your data to a third party to act
An MCP server in-house and a model inside your perimeter: the agent acts, nothing leaves
Data that has to pass through a third-party cloud
Private models inside your infrastructure
A POC running on a laptop
A deployed, scalable, monitored system
The stack
Docker
AWS
GCP
Azure
Ollama
vLLM
Terraform
Vercel
Straight answers
Can my data stay entirely in-house?
Yes. I deploy open-source models directly inside your infrastructure: nothing leaves your perimeter, not the documents, not the questions people ask. Together we weigh cost, performance and sovereignty.
Is a private model as good as GPT or Claude?
For many focused tasks, yes: classification, extraction, RAG on a specific domain. For complex reasoning, the large models keep the edge. It's a trade-off we measure on your data, not a matter of belief.
Is AI compatible with GDPR?
Yes, provided the architecture is right: EU hosting, private models, data processing agreements. For me it's a design criterion from day one, not a constraint you discover at the end.
Is a European model enough to be sovereign?
Not always. Many models, European ones included, run on US clouds like Azure and then fall under the US Cloud Act: in theory, a US authority can request access. Real sovereignty comes down to where the model AND the MCP server that acts on your tools actually run. I check this before advising you, and I switch to an EU cloud or on-prem when your data requires it.
Contact
Ready to go from demo to production?
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