On-prem AI Infrastructure for European Enterprises

A customer-premises AI appliance with local inference, a compact operational Console, an observability layer and supported APIs for your existing applications. Connect your favourite apps and harnesses. Nothing leaves your infrastructure.

Talk to sales → Explore the technology
$0B+
Enterprise AI market by 2028.
35%+ annual growth — IDC
0%
Of enterprises report cloud AI costs over budget.
Gartner, 2024
€0B
In GDPR fines — AI data handling is the next frontier.
EU regulatory exposure
00 — What you get

A compact inference core in one appliance.

LLM Machines brings local model serving, operational control and application access together inside your infrastructure.

Supported API

Familiar endpoints, local models.

Use /models and streaming or non-streaming /chat/completions, including tool-call message transport.

Applications

Connect your favourite apps and harnesses.

Issue Application credentials, set model access and keep the client applications and workflows your teams already use.

Agentic runtime

LXC runtime for agentic workflows.

Run agentic workflows in a dedicated local LXC runtime integrated with the supported inference API.

Console

Compact operational control.

Manage Applications, inference, hardware, team access, Activity & Audit and settings from one operational surface.

Operations

Local observability layer.

Monitor model health, capacity, request metadata and alerts without sending operational telemetry elsewhere.

Sovereignty

Nothing leaves your infrastructure.

Models, API traffic, credentials and operational records stay inside the customer-controlled perimeter.

01 — The problem

Enterprise AI has a control problem.

External processing, provider-specific integrations and sensitive data paths make regulated adoption harder to govern.

External operating dependency.

Public AI services place inference availability, model access and operational policy under a provider's control.

Provider-specific integration.

Provider-specific APIs, model choices and policy controls can make switching costly. A documented API contract and portable components reduce that dependency.

Your data in their cloud.

Every prompt sent to a cloud LLM is processed on someone else's servers. Trade secrets, customer PII, and confidential data all flow through infrastructure you don't control.

[ 01 ]

Nothing leaves your infrastructure.

Inference, credentials, operational records and application traffic remain inside your perimeter.

[ 02 ]

A documented application API.

Connect existing applications through Models and streaming or non-streaming Chat Completions endpoints.

[ 03 ]

The supported core in one box.

Inference gateway, local model serving, operational Console, observability layer and LXC agentic runtime are integrated and tested together.

[ 04 ]

Your apps and workflows stay the same.

LLM Machines provides local inference and a documented API contract. Connect the client applications and harnesses your teams already know.

03 — Sovereignty

Built in the EU.
Built for the EU.

Sovereignty isn't a feature — it's the foundation. Every default is designed to support regulated European operation, with deployment-specific evidence and review still required.

Read more →
EU AI Act
Metadata-only audit, transparent model access and human operational control make deployments inspectable inside your perimeter.
GDPR
Nothing leaves your infrastructure. Processing, credentials and operational records remain under customer control.
NIS2
Remove the public-cloud model dependency from the AI supply chain and keep operations locally observable.
EU Data Act
An open-source foundation, documented API contract and signed offline entitlement support portability and operational control.
04 — Technology

A compact operational core.

One appliance brings inference, identity, operational control and lifecycle tooling together behind a documented interface.

See the architecture →
Inference API

Connect your favourite apps and harnesses.

Use /models and streaming or non-streaming /chat/completions with stable Application credentials and tool-call transport.

Emergency Isolation Mode

Protect the core while keeping operations available.

Emergency Isolation Mode blocks Application inference and agentic access while Console, identity, audit, hardware monitoring, backup and recovery remain available.

Agentic workflows

A dedicated LXC agentic runtime.

Run local agentic workflows in an isolated LXC runtime connected to the same supported inference contract.

05 — Why not the cloud

Why not just use the cloud?

Enterprises face a painful trade-off — until now.

Azure AI / Bedrock Build in-house LLM Machines
Data sovereignty Data leaves org Possible Nothing leaves
Operating model Provider-managed Self-assembled Owned capacity
Vendor independence Provider-dependent If built right Open-source stack
Deployment approach Service activation Build and integrate Structured onboarding
Supported inference core Assemble services Build everything Integrated appliance
Operational controls Complex add-ons Possible Local by design
06 — Build vs. partner

The build-it-yourself workload.

Compare assembling every platform layer internally with deploying an integrated, supported inference core.

See the full breakdown →

Building it in-house means integrating and operating every layer. Partnering with us means starting from a signed, supported core.

Build in-house
Build every layer
Partner with us
Integrated delivery
07 — Deployment

Configured to your workload.

Each appliance is sized around your models, concurrency, latency targets, storage, identity, network and operational requirements.

See how deployments are scoped →
Appliance scope
Models & capacity
Model selection, concurrency, latency, throughput and hardware sizing.
Identity & Applications
Enterprise identity, Application credentials, model access and existing client connectivity.
Operations & recovery
Observability, audit, signed updates, backup, recovery and Emergency Isolation Mode.
Core Appliance
Local inference · operational control

A signed inference core with the Console, identity, observability and lifecycle tooling inside your infrastructure.

LXC agentic runtime
Local · isolated · integrated

A dedicated LXC runtime for agentic workflows using the supported local inference API.

Apps & Harnesses
Keep your client workflows

Connect the applications, developer tools and automation harnesses your teams already use.

08 — Onboarding

From discovery to controlled handoff.

A tailored customer-premises deployment with documented validation at every stage.

See onboarding →
00
Before delivery
Pre-shipment

Discovery questionnaire, workload sizing, network pre-flight and signed offline entitlement planning.

01–02
Installation
Hardware & first boot

Receive, rack, power, activate the signed entitlement and complete appliance self-tests.

03–05
Platform configuration
Federate & integrate

Identity federation, Applications, model access, favourite client tools and the LXC agentic runtime.

06–08
Validation & handoff
Validate & partner

API, isolation, backup and recovery tests followed by training and operational handoff.

09 — Timing

Why now.

The market is reaching a tipping point.

10 — Company

European, by design.

A small, technical team building sovereign AI infrastructure for European enterprises. Bootstrapped on conviction.

About the company →
Mission

To make sovereign AI practical for European enterprises through an appliance they own, audit and control.

Headquarters
Flag of Croatia
Croatia · European Union Private AI infrastructure for EU-regulated teams European startup
11 — FAQ

Questions enterprises ask first.

Short answers for teams comparing on-prem AI, private AI platforms and cloud LLM APIs.

What is an on-prem AI appliance?

An on-prem AI appliance is a pre-integrated hardware and software stack for local model serving, supported API access, identity, operational control, observability, updates, backup and recovery inside your own infrastructure.

Does company data leave our network?

No. Nothing leaves your infrastructure. Models, API traffic, credentials and operational records remain inside your perimeter.

Which API endpoints are supported?

Yes. Connect your favourite apps and harnesses through /models and streaming or non-streaming /chat/completions, including tool-call message transport.

How long does deployment take?

Deployment follows a documented sequence covering discovery, sizing, installation, identity, Application credentials, validation, training and operational handoff.

Which models are supported?

The stack is built for open-weight models such as Llama, Mistral, Qwen and similar families, with routing and serving handled through the local inference layer.

How is each appliance configured?

Each appliance is sized around your models, concurrency, latency targets, storage, identity, network and operational requirements.

12 — Bring AI in-house

Sovereign AI, deployed in your infrastructure.

Pilot the appliance with your existing applications and workflows. Local inference runs inside your perimeter. Nothing leaves your infrastructure.

Talk to sales → Explore the technology