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  LOCAL LLMS · ON-PREMISE

AI that never leaves your data center.

Language models run on-premise — on your hardware, with your data. Local open-weight models, optionally extended with commercial frontier models over secure interfaces when it makes sense. No data leaving, full control.

01 Principle

Data stays in house.

AI should never be the reason you give up data sovereignty. Models run where your data already lives — on your infrastructure, under your control.

Local inference

Models run on your own hardware. Prompts and documents never leave your network.

Data sovereignty

No sharing with third parties, no training on your data. GDPR-friendly by design.

Hybrid when it helps

Sensitive work stays local; for the rest, frontier models can be added over secure APIs.

Open weights

Open-weight models instead of a black box — swappable, inspectable, no vendor lock-in.

02 Platform

More than a model.

A usable AI platform needs more than inference — gateway, knowledge, access and operations all belong to it. We deliver the whole stack.

Local inference

Efficient serving of open-weight models on GPU — from compact to large.

Model gateway

One entry point for many models: routing, quotas, keys and a cost overview, centralised.

Embeddings & vector search

Your content made searchable — semantic, fast and fully local.

RAG & knowledge

Answers grounded in your documents — with source references instead of made-up claims.

Chat interface

A self-hosted interface for your team — familiar to use, entirely in your hands.

SSO & access control

Sign-in via your identity system, with roles and rights per team and use case.

Observability & cost

Utilisation, latency and consumption in view — capacity plannable, costs transparent.

Workflow automation

Models as building blocks in automated processes — wired into your existing systems.

03 Delivery

From hardware to operations.

We build the platform as versioned code and run it day-to-day — or hand it over cleanly to your team.

  1. /01

    Needs & sizing

    Use cases, privacy requirements and the right hardware and model size to match.

  2. /02

    Platform deployment

    The whole stack as Infrastructure as Code — built reproducibly, not clicked together.

  3. /03

    Model selection & tuning

    The right models chosen and tuned to your tasks — quality weighed against resources.

  4. /04

    Knowledge integration

    Your documents and data sources indexed and made usable through retrieval.

  5. /05

    Access & SSO

    Integration with your identity system, a role model and secured endpoints.

  6. /06

    Operations & monitoring

    Updates, scaling, backups and observability — or training for self-operation.

On-premise

Models and data stay on your hardware, in your data center.

Open weights

Swappable models without lock-in instead of an opaque black box.

Hybrid optional

Frontier models only where needed — deliberately, not by default.

Observable

Consumption and cost transparent — no surprises at the end of the month.

04 / Enquire Direct

Your AI. Your data. Your infrastructure.

Tell us briefly about your use case — we'll propose hardware, models and a platform build.