[ 01 ]
Data and hosting map
For each workflow: what data goes in, where inference runs, which provider or model sees it and what is stored.
Sovereign & local AI
You choose where the data lives and where the model runs.
Custom AI on infrastructure you control: your cloud region, your servers or your laptop fleet. Local models for the cases that cannot leave, provider APIs where they can.
Inference location chosen per use caseOpen-weight and provider modelsGoverned and logged
[ 01 — WHAT YOU GET ]
Control you can point to.
Sovereign does not mean everything on-prem. It means each workflow has a documented answer to where data goes.
- [ 02 ]
Deployment options
Your cloud tenant and region, a private endpoint, servers you operate or a local model on managed devices. - [ 03 ]
Model selection
Open-weight models such as Llama, Mistral or Qwen families next to provider models. We test them on your tasks and report the quality gap. - [ 04 ]
Governed access
Your identities, roles and logging in front of every model, so use is attributable and reviewable. - [ 05 ]
Honest trade-offs
Local models cost hardware and upkeep, and are weaker on some tasks. You get the numbers before you commit.
[ 02 — PROCESS ]
Decide with evidence.
We compare options on your own tasks, not on benchmarks.
- 01
Classify the data
Which data may go to a provider, which only to your cloud region, which must stay inside.1 to 2 weeks - 02
Test the models
We run a sample of your tasks through local and hosted candidates and compare quality, speed and cost.2 weeks - 03
Pilot the setup
One workflow on the chosen architecture, with logging and access control in place.4 weeks - 04
Hand over
Runbook, monitoring and a named owner. You can operate it, or we can, under a separate agreement.1 to 2 weeks
[ 03 — USE CASES ]
Cases where it matters.
Typical scenarios. None of these describe a specific client.
Internal knowledge assistant
- Today
- Staff cannot use public chatbots on internal documents.
- With the workflow
- A private assistant answers from approved documents and cites sources. It respects existing file permissions.
Sensitive document processing
- Today
- Contracts or case files are read by hand because they cannot go to an external API.
- With the workflow
- A local model extracts fields and summaries on your own servers. A person confirms each result.
Regional hosting requirement
- Today
- A customer contract requires processing in a defined region.
- With the workflow
- The workflow runs on a model deployed in that region, with a log that shows where each request ran.
[ 04 — SCOPE & PRICE ]
Architecture first, build second.
Sovereign AI architecture & setup · 6 to 8 weeks
from CHF 14,000fixed scope · excl. VAT
Hardware and inference costs are separate. Extensions: CHF 2,200 per day.
- Data and hosting map
- Model tests on your tasks
- One workflow on the chosen setup
- Runbook and named owner
[ 05 — QUESTIONS ]
What people ask about sovereign AI.
Is local AI as good as the big models?
Not on every task. For narrow, well-defined work such as extraction or classification, compact local models often do well. For open-ended reasoning, provider models are usually stronger. We measure this on your tasks.
Do we need our own GPUs?
Not always. Options range from a private cloud endpoint to a single server or workstation. The audit sizes it against your volume.
Does a provider API mean our data is used for training?
Business API terms of the main providers generally exclude training on your data, but terms differ and change. We read them with you per use case and document the outcome.
Who maintains it?
You can, with a runbook we hand over, or we can under a separate support agreement. Updates to models are planned, not automatic.
Can this run in our Microsoft tenant?
Yes, for example with Azure AI Foundry in your region. Tenant operations remain with worxspace or your IT team.
Tell us what cannot leave the building.
We show you what is possible on your own infrastructure, and what it costs.