On-premise AI
Private AI on client-owned hardware: open-weight models behind your firewall, data sovereignty by architecture rather than by contract.
The problem
For some organizations, sending data to a cloud AI is simply not on the table - client confidentiality, regulatory constraints, contractual promises, or plain prudence rule it out. The usual conclusion is to skip AI entirely. That conclusion is out of date: open-weight models have become genuinely capable, and inference on owned hardware is a solved engineering problem.
Sovereignty here is architectural, not contractual. A data-processing agreement promises your data is handled well; a server in your building makes the question moot. EU and Swiss data-protection rules become dramatically simpler when the data never crosses your property line.
Engagement
We start with your tasks and your constraints - not with hardware. You get a feasibility read on real samples: which tasks open-weight models handle today, which they do not, and what the setup costs in hardware and maintenance. If the answer is "the cloud model is the right call for you," we say so. The build itself is fixed-scope: deployment, retrieval, workflows, guardrails, and a handover your IT team owns.
Start by email: what to include is on the contact page.
What we ship
- Hardware sizing and open-weight model selection matched to your actual tasks
- Inference deployment on your servers - quantized, monitored, updatable
- Retrieval over your private documents, fully inside your network
- Human-in-the-loop workflows for the steps that carry liability
- Air-gapped operation where required - no external calls, verifiable
Proof
- Building uniside.org with agentsCase study
- AzethPortfolio entry · testnet alpha
Common questions
Which models do you deploy?
Current open-weight models chosen per task - text, code, extraction, or retrieval each favor different ones, and the honest answer changes every few months. We benchmark candidates on your real documents before committing, and the deployment is built so the model can be swapped as better weights appear.
What hardware does this need?
Anywhere from a single GPU workstation to a small server rack, depending on concurrent load and model size. We size against your measured workload, not against a vendor's reference architecture - many defined tasks run well on hardware that fits under a desk.
Is on-premise AI weaker than the cloud frontier models?
For open-ended reasoning, yes - hosted frontier models are ahead. For defined tasks over your own documents and processes, well-chosen open weights are routinely sufficient, and the difference often stops mattering. We tell you which side of that line your use case is on before you buy hardware.
Who can access our data?
Inside your building: whoever you authorize. Outside it: nobody - that is the point of the architecture. Nothing is sent to us, to model vendors, or to any cloud; where required we deliver fully offline systems whose network silence you can verify yourself.
How do updates work without a cloud connection?
As scheduled maintenance you control: model weights, inference server, and evaluation suite are versioned together and updated from installation media or a controlled network window. Every update re-runs your acceptance benchmarks before it goes live.
Industries
last edited 2026-07-02