Digital Sovereignty - Local LLMs. Zero External Dependencies.
OFFERINGS:
LOCAL LLM DEVELOPMENT
WHY
The threat isn't the model. It's the data path.
Every organisation wants AI on their operational data. But every external API call is a channel you chose to open. A processor you chose to trust. A jurisdiction you chose to accept.
Under GDPR, that channel requires a processor agreement, a legal basis for transfer, and a data transfer impact assessment if the provider sits outside the EU. Under NIS2, it's a dependency you must formally assess and document.
For most organisations, that's a calculated risk. For regulated sectors - health, energy, finance, critical infrastructure - it's a risk they cannot take.
IN BRIEF
Why:
Sensitive data member records, contracts, case files, financial reporting. This is where the real AI value is. But it's also where the risk is highest.
Cloud APIs mean third-party processors, data residency questions, and a compliance surface that grows with every call.
Most AI initiatives stall here. Not because the technology isn't ready, but because legal can't sign off on the data leaving the building.
What:
A local LLM running on your infrastructure. The data never leaves. No processor agreements to negotiate. No sub-processor chains to audit.
Privacy isn't a feature you enable, but it's the actual architecture. Combined with cloud AI for non-sensitive workloads.
The goal is to get both control and scale.
How:
We start by understanding your data landscape. What's sensitive, what isn't, and where the boundaries are.
Then we design a hybrid architecture: open-weight models running locally where it matters, cloud AI where it makes sense, and clear governance for both.
The goal is production, not proof-of-concept.
what we know:
1.
There is no one-size-fits-all architecture.
The split between local and cloud looks different for every organisation. It depends on your data, your policies, your infrastructure, and your use cases.
Anyone who sells you a standard setup hasn't looked at your data.
We experiment and iterate to find what actually works. Not what works in a demo.
2.
Local doesn't mean limited.
Open-weight models are closing the gap on commercial models.
The tooling is maturing fast. What used to be a research project is now a deployment question.
The real unlock isn't running a model locally. It's letting AI loose on the data that actually drives your organisation.
3.
Compliance is the door. Not the destination.
Most AI initiatives on sensitive data stop at legal. And for good reason, the compliance question has to be answered first.
But once the architecture is in place, something bigger opens up. Internal documents, member data, case records, financial reporting.
The data that was always off-limits to AI is suddenly accessible. That's where the real value starts.
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