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Healthcare · insurance · legal · public sector

The AI that never leaves your building

Your data cannot go to an American vendor — and you are right about that. The result: AI is officially banned, and unofficially used on personal phones, with your confidential documents in it. The risk has not gone away. It has become invisible.

Watch the pipeline run
Today
38 min / case
Time to production
6 to 10 weeks
What stays human
Nothing is decided by the machine. On a regulated case, the AI prepares and retrieves; the responsible person signs.

Before / after

The same process, run twice

On the left, what it costs today. On the right, the pipeline played step by step, with the data that actually moves from one step to the next.

Today · by hand

38 minutes per case

  1. Hunt for the information across the files14 min
  2. Write the summary by hand16 min
  3. Have it reviewed for compliance8 min

A working assumption, not a measurement taken at your company. You’ll adjust these figures below.

Pipeline · ia-sans-fuite-de-donnees

  1. The document stays with you

    —

    On your server, or in a Swiss or European datacentre you choose.

    Data perimeter

    Storage      : your infrastructure
Processing   : your infrastructure
Outbound traffic to a third-party vendor: none
AI sub-processor to declare under GDPR: none

  2. The model runs locally

    1.8 s per answer

    An open model executed on a dedicated GPU, on site. No external API, no per-request cost.

    Execution

    engine        : local inference on dedicated GPU
model         : open model, versioned and pinned by you
external_api_calls : 0
cost_per_request   : €0 (hardware amortised)
training_data      : no client data reused

  3. The answer is produced

    real time

    The same usefulness as a consumer assistant — on files you could never have pasted into one.

    Case summary

    “Case no. 2026-1187 · summary as of 12/11
Three missing documents identified, including the employer
certificate (chased on 04/11, no reply).
Statutory deadline: 28/11.”

sources: 4 documents from the case file, cited

  4. Everything is logged

    —

    Who asked what, when, on which file. An audit trail you can show an inspector.

    Audit log

    2026-11-12 09:41 · officer M.D. · case 2026-1187 · summary
Timestamp, user, document, action — all retained
Export for audit or supervisory authority
Retention configured to your policy

  5. Shadow use ends

    —

    Teams finally have a sanctioned tool. Shadow IT loses its reason to exist.

    What this fixes

    No more confidential documents pasted into a public chat
An AI policy you can enforce, because an alternative exists
Predictable cost: no per-token billing
Pinned model: behaviour doesn’t change overnight

Nothing is decided by the machine. On a regulated case, the AI prepares and retrieves; the responsible person signs.

Your numbers

What this weighs at your company

Run the numbers on your own figures

Move the sliders: everything recalculates live, from your own volumes.

300cases/mois
38min
70€/h
60%
What that adds up to

0h/month

Time handed back to your team, every month

0€/year

Value of the time recovered over twelve months

0FTE

Full-time equivalent freed from work that creates no value

0€

What doing nothing costs over three years

Formula: volume × time per item × share automated × loaded hourly cost. The result is only as good as your assumptions — which is exactly what we check together during scoping.

Put these numbers to the test

How we build it

Nothing magic — assembled building blocks

Every block is replaceable and documented. You own the code, the data and the access — that is a condition, not an option.

See the offering Assistants & RAG
  • Local inference on a dedicated GPU (Ollama / vLLM)
  • No outbound network calls
  • Timestamped audit log
  • Role-based access control
  • Hosted on your premises, in Switzerland or in the EU

Proof

We operate our own local inference platform on a dedicated GPU, with self-hosted models and monitoring — this is not a theoretical capability.

The other scenarios

Often, the real subject is somewhere else

One concrete problem is enough to start

Shall we test these assumptions on your case?

Twenty minutes is enough to know whether this scenario holds at your company, what it assumes about your data, and where to start.

Describe my project