Solution
Our data cannot leave
Data residency and on-premises AI
For a large amount of genuinely useful material - case files, personnel records, client correspondence, source code - the question is not whether AI would help. It is whether the data can be sent to a third party at all, and the answer is often decided by a contract or a regulation rather than by preference.
That question is separable from the model. Where inference runs, who can see the material, and which provider you are tied to are three different decisions, and treating them as one is how organizations end up with a dependency they did not intend to take on.
You are probably here because
- The material that would benefit most from AI is the material you cannot send anywhere.
- A data processing agreement or a sector rule sets a boundary the obvious tools cross.
- Different teams have each picked a different provider, and nobody owns the overall exposure.
- You want the option to change model or provider later without rebuilding what sits on top.
What settles it
Deciding where inference has to run
Separating what is genuinely constrained by residency and contract from what has simply never been examined. The constrained set is usually smaller than assumed, which changes the economics of the whole design.
Models on your own infrastructure
Running open-weight models inside your own environment or a controlled tenancy for the material that cannot leave, sized against what the workload actually needs rather than against a benchmark.
A gateway between applications and models
One internal interface where routing, access control, logging, and policy live. It is what keeps the provider decision reversible, and what makes the exposure answerable in one place instead of per team.
Retrieval that respects existing access rules
Search and grounding that only ever surface what the individual user is already permitted to see, so the layer meant to make knowledge findable does not quietly become a way around access control.
Answers traceable to their source
Output linked back to the documents it was drawn from, which is both what makes it checkable and what makes it usable in work where the source has to be cited.
Where data sits, who can compel access to it, which provider you depend on, and whether you could change your mind later are four separate questions. Establish which of them actually bind you before choosing a hosting model. Usually it is one or two, not all four.
how this gets delivered
Capabilities, cases, and further reading
Delivered through
- Operational AI
AI systems that integrate with existing platforms and workflows, with control, traceability, and operational reliability.
- Platform Engineering
Reliable and maintainable platform foundations that support development and operations at scale.
Where we have done it
- Sovereign AI Gateway
A unified gateway that centralizes model routing, policy enforcement, and auditability.
- Secure RAG System
Secure retrieval architecture for trusted, role-aware access to internal knowledge.
Further reading
- On-premises vs hosted LLM: how to choose
The choice is usually settled by data residency and contracts, not by cost or model quality. Here is what each option costs you in practice.
- What sovereign AI actually means
The term covers four separable properties, and vendors tend to sell the cheapest one. Which of them you actually need depends on what you are protecting against.
- Sovereign AI in regulated EU enterprises
Data residency and modern AI are not mutually exclusive. A governed model gateway and on-premises deployment let regulated organizations use AI on their terms.
- What an AI gateway does, and when you need one
One internal interface between your applications and whichever model serves them. What belongs in that layer, and the point at which not having one starts to cost you.
faq
Frequently asked questions
- Is an EU-hosted service enough, or does it have to be on our own hardware?
- It depends on which property you actually need. EU hosting settles the physical location of the data. It does not by itself settle who could be compelled to hand it over, or how quickly you could move away from that provider. Deciding which of those matters for the material in question usually resolves the question faster than debating hosting models in the abstract.
- Are open-weight models good enough for real work?
- For a great deal of operational work, yes, particularly where the task is grounded in your own documents rather than relying on the model's general knowledge. The honest framing is that the gap is real but narrower than it was, and it is worth measuring against your actual task rather than against a leaderboard.
- What does running our own models actually cost?
- The hardware is usually the smaller part. The recurring cost is operating it: capacity, upgrades, evaluation, and the people who keep it running. Where the constrained set of material is small, a hybrid arrangement is often cheaper than either extreme, and that is worth calculating before committing.
- Can we start hosted and move later?
- Yes, if the gateway is in place from the beginning. What makes migration expensive is not the model; it is the dozens of places application code has been written against one provider's interface. Keeping that surface in one place is the decision that preserves the option.