On-prem AI Infrastructure for European Enterprises

A private AI appliance with local inference, an OpenAI-compatible gateway, RAG, agents, audit logs, PII redaction and MCP connectors. Deploy inside your perimeter — no cloud exposure, no per-token bills, EU AI Act, GDPR, NIS2 and Data Act ready by design.

Talk to sales → Explore the technology

$0B+

Enterprise AI market by 2028.

35%+ annual growth — IDC

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 complete private AI stack in one appliance.

LLM Machines packages the core services enterprises need to run generative AI locally, with clear support boundaries and no dependency on public-cloud LLM APIs.

API gateway

OpenAI-compatible endpoints.

Point existing tools at a private gateway for local models, usage tracking, rate limiting, audit logging and team-level controls.

Knowledge

RAG over internal data.

Connect documents, wikis, tickets and repositories so answers are grounded in your own knowledge base without moving data out.

Automation

Agents and workflows.

Run controlled agentic tasks, governed workflows and MCP tools inside your network, with credentials stored on the appliance.

Privacy

PII redaction layer.

Detect and anonymise sensitive personal data before prompts reach models, with policies aligned to regulated European workloads.

Governance

Audit logs by default.

Keep prompt, response, user, model and routing records inside your perimeter so security teams can inspect what happened.

Connectors

Vetted MCP catalog.

Wire chat, source control, ticketing, docs, CRM, mail and storage through curated connectors instead of ad hoc integrations.

01 — The problem

Enterprise AI is broken.

Three forces are making enterprise AI painful, expensive, and risky.

Bills through the roof.

Cloud AI costs are unpredictable and punishing at scale. A company processing millions of tokens per day can spend hundreds of thousands of euros per year — with bills that grow every month.

Dangerous vendor lock-in.

Once you build on OpenAI, Azure, or AWS, you're trapped. Changing providers means rewriting your entire stack. These platforms are engineered to keep you dependent.

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 ]

Data never leaves your perimeter.

Full on-premise or private cloud deployment. Zero cloud exposure. EU AI Act, GDPR, NIS2 and EU Data Act ready — by design, not retrofitted.

[ 02 ]

No per-token bills — ever.

One-time license. Run unlimited inference. Costs scale with your hardware, not your query volume.

[ 03 ]

The complete stack in one box.

Gateway, inference, RAG, agents, PII anonymisation — all integrated, tested, and ready to deploy in days.

[ 04 ]

Zero vendor lock-in.

Built entirely on auditable open-source components. Swap models, extend the stack, or self-support at any time.

03 — Sovereignty

Built in the EU.
Built for the EU.

Sovereignty isn't a feature — it's the foundation. Every default is engineered to satisfy the four pillars of European digital regulation.

EU AI Act

Audit trails, transparency, human oversight — on-prem deployment makes high-risk systems inspectable inside your perimeter.

GDPR

Personal data never leaves your network. Microsoft Presidio detects and redacts before models see anything.

NIS2

Eliminate the cloud-AI link from your supply chain. Board-level cybersecurity exposure removed by default.

EU Data Act

Open-source foundation + one-time license honour the Act's portability and switching mandates by default.

04 — Technology

Twelve layers of certified core.

One appliance. A signed manifest enforces what runs where — and Pure Mode keeps anything custom from blocking your SLA.

See the architecture →

Tier model

T1 / T2 / T3 with manifest enforcement.

Every component is signed and labelled. T1 runs with host privileges. T2 in restricted containers. T3 sandboxed with no host access. Tier badges show in the admin UI next to every component.

Pure Mode

Kill everything custom. Keep certified core running.

One-click admin action that disables every T2/T3 component. Use it for security incidents, support diagnosis, or to keep an audit clean.

MCP catalog

Vetted connectors out of the box.

Chat, source control, ticketing, docs, CRM, mail, storage — wired through curated MCP servers. Every credential lives in your on-box vault.

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	Guaranteed
Predictable cost	Per-token billing	Fixed infra	One-time license
Vendor independence	Locked in	If built right	Open-source stack
Time to deploy	Days	Months to years	Days to weeks
Full-stack AI	Partial	Build everything	All-in-one
Compliance ready	Complex add-ons	Possible	PII layer built in

06 — Build vs. partner

The build-it-yourself maths.

What sovereign on-prem AI actually costs to ship internally — versus what we charge to ship it for you.

See the full breakdown →

Building it in-house: €600K–€1M+ in Year 1. Partnering with us: ~6.5% of that, hardware passed through at zero commission.

Build in-house

€600K – €1M+

Partner with us

~€41K + HW

07 — Pricing

Custom-quoted to your usage profile.

Every quote is sized to your actual usage and capacity. No public price list — but here are the floors:

See pricing →

Pricing floor

From €5,000

One-time setup & configuration. Discovery, install, identity federation, connector wiring, validation, training.

From €3,000 / month

Recurring service retainer — scales with your hardware footprint, user count, and SLA.

0% on hardware

Zero commission on the Supermicro pass-through. Your infrastructure investment stays with you, not us.

Gateway

No GPU · gateway-only

Teams already using external LLM APIs who need a private gateway, usage tracking, and PII redaction.

SME Appliance

Single GPU · fully local AI

Mid-size companies wanting fully local AI for engineering teams of 10–100 users.

Enterprise

Multi-node · HA · custom SLA

Large enterprises and regulated industries needing high-availability, horizontal scaling, dedicated SLAs.

08 — Onboarding

Signed contract to live system: 4–6 weeks.

A tailored deployment, not a SaaS sign-up. Eight phases, each customised to your environment.

See onboarding →

T-14 → T-0

Pre-shipment

Discovery questionnaire. Network pre-flight. License key bound to your hardware.

01–02

Day 0

Hardware & first boot

Receive, rack, power, license, self-test. ~2.5 hours total.

03–05

Day 1 → Day 2

Federate & integrate

Identity federation, app stack online, connectors wired and smoke-tested.

06–08

Day 2 → 30 days

Validate & partner

End-to-end tests, training, handoff, calendar-locked 30-day check-in.

09 — Timing

Why now.

The market is reaching a tipping point.

→ 01 EU AI Act, GDPR, NIS2 and the EU Data Act are pushing regulated industries to keep AI workloads inside their perimeter — and enforcement is sharpening year over year.
→ 02 Open-weight models — Llama, Mistral, Qwen — now match GPT-4-class quality, making on-premise inference viable.
→ 03 Enterprise security teams are blocking cloud AI tools, creating urgent demand for internal alternatives.
→ 04 AI spend anxiety: businesses moving from unpredictable OPEX cloud bills to controlled CAPEX hardware.

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 the default for European enterprises — by replacing the cloud-AI tax with an appliance you own, audit, and control.

Headquarters

Croatia · European Union EU AI Act · GDPR · NIS2 · EU Data Act — ready by design

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 that runs models, API gateways, RAG, agents and controls inside your own data centre or private environment.

Does company data leave our network?

No. The default deployment keeps prompts, documents, model traffic, logs and credentials inside your perimeter. We do not need data-plane visibility to support the appliance.

Is it compatible with OpenAI-style tooling?

Yes. The gateway exposes OpenAI-compatible endpoints so teams can point existing applications, developer tools and automation frameworks at local or approved models.

How long does deployment take?

A typical deployment moves from signed contract to live system in 4–6 weeks, including discovery, sizing, installation, SSO, connectors, validation and 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 does pricing work?

Pricing starts from a setup fee and service retainer sized to your usage profile, hardware footprint, user count and SLA. Hardware is passed through at zero commission.

12 — Bring AI in-house

Sovereign AI, deployed in days.

Pilot the appliance against your real workloads. We'll deploy it inside your perimeter — no cloud exposure, no per-token bills.