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Product · On-premise & GDPR by design

Your own AI. Your house. Your data.

A fully local language model on your own hardware — open source, offline-capable, with its own knowledge base. Capture the value of AI without handing trade secrets and personal data to third-party clouds. The model is downloaded once; after that the system can run entirely offline.

The architecture

Four building blocks — all running locally.

All open source — transparent, auditable, replaceable. No black-box vendor.

01 · LLM

Mistral

The open-source language model (Apache 2.0) from EU provider Mistral AI — multilingual, generates the actual answers, runs fully locally.

02 · Model server

Ollama

The runtime that loads and runs the model — uses the built-in Apple GPU on the Mac. One command loads or swaps models.

03 · Interface

Open WebUI

A browser interface like ChatGPT — user accounts and permissions, multilingual, with the built-in knowledge base for your own documents.

04 · Knowledge base

RAG + Embeddings

Your own documents and emails are turned into searchable vectors locally (nomic-embed-text) and given to the model as context — no upload, no retraining.

100 % local
No data leaves the house · no telemetry · model downloaded once, then operable offline. Packaged reproducibly with Docker.
The value

Data sovereignty, predictable cost, independence.

Sovereignty

Data stays in the house

No transfer to third parties. Trade secrets, personal and client data stay under your own control.

Cost

No per-request fees

No token price, no per-seat licence. Runs on existing hardware — predictable fixed cost.

Independence

No vendor lock-in

Open source, interchangeable models. No dependency on one provider, its prices or terms.

Relevance

Knows your knowledge

RAG binds in your own documents and emails — answers with your context, not generic general knowledge.

Language

Multilingual

Ask and answer in any language — practical for international teams and correspondence.

Compliance

DSGVO by Design

Processing in-house / in the EU, no third-country transfers, no telemetry — data protection is built in.

Use scenarios

Connect any of your own sources — everything stays in the house.

Support · speech-to-text

Help desk with instant answers

Customers describe their issue by voice; the model delivers a solution from manuals, FAQs and past tickets — 24/7, multilingual.

Field service

Assistant for technicians

On-site queries for installation, wiring and repair instructions — from technical docs, schematics and manuals. Also for training and onboarding.

Sales & back office

Offer & document assistant

Draft offers and reports from templates and history, check contracts and tenders against requirements, summarise long documents in seconds.

Connectable sources
Email mailboxes · file & network shares (NAS/SMB) · local folders · wiki / intranet / SharePoint · contracts, PDFs, manuals · databases / CRM / ERP — everything stays in the house.
Security

Secure — and provable.

Not “we promise” but architecturally enforced and verifiable.

01

Configuration

Everything that phones home is switched off — no telemetry, no external AI services, no web calls. Local accounts and access rights.

02

Architecture

Airgap possible: the system can run fully disconnected from the internet — data leakage is then physically impossible.

03

Proof

Verifiable, not a matter of faith: a network capture shows zero outbound connections. All on your own, encryptable hardware.

An honest comparison

Not either/or — the right tool for the data.

An internal LLM is not a replacement for everything. Transparency about the trade-offs is part of the deal.

Internal LLM — its strengths
  • Data protection: everything stays in the house / EU.
  • Fixed cost, no per-request fee.
  • Fully offline / airgap-capable.
  • Strong for everyday work and knowledge search on your own data.
Where public cloud AI is ahead — the limits
  • Top-tier reasoning: large cloud models are stronger on highly complex tasks.
  • No live web — the knowledge has a cut-off date.
  • Like any LLM it can hallucinate — RAG and a checking eye stay important.
  • Operation, updates and concurrent-user capacity are your own responsibility (hardware-bound).
Our recommendation
Combine deliberately: the internal LLM for sensitive data (personal, trade/client secrets, internal documents); public cloud AI, contractually secured, for non-sensitive complexity. Always ground answers in your own data (RAG), check results, control access — AI assists, the human decides.

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