Managed Private Inference
Inside Your Infrastructure

We configure, maintain, monitor, update and optimize private inference inside infrastructure you control. Choose the models. Connect your favourite apps and harnesses through a documented API. 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

Managed private inference, end to end.

From workload sizing and distributor-backed Supermicro procurement to inference configuration, observability, updates and recovery.

Supported API

Familiar protocol, approved local models.

Chat Completions is the documented API protocol your favourite apps and harnesses use to access approved local models.

Managed operations

Configured and maintained for you.

We configure, monitor, update and optimize the supported inference environment around your operating requirements.

Infrastructure

Distributor-backed Supermicro procurement.

We size enterprise Supermicro infrastructure and coordinate fulfilment through a distributor-backed channel.

Operational control

One compact Console.

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

Source rights

Inspectable first-party Product source.

Original first-party LLM Machines Product source is source-available under the unmodified PolyForm Internal Use License 1.0.0. Third-party components retain their upstream licences.

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 inference API.

Your existing apps and harnesses connect through the documented Chat Completions protocol and access only approved local models.

[ 03 ]

A managed operational foundation.

Local model serving, gateway and policy controls, the operational Console, observability, updates and recovery are integrated and tested together.

[ 04 ]

Your applications stay yours.

Connect the applications and harnesses your teams already know through the documented API. Outcome-led integration consulting is scoped separately when needed.

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
A documented API contract, portable models, upstream-licensed third-party components and signed offline release and recovery paths support portability and operational control.
04 — Technology

A managed operational foundation.

Private inference, identity, operational control and lifecycle tooling stay together behind a documented interface.

See the architecture →
Documented API

Connect your favourite apps and harnesses.

Chat Completions is the documented API protocol they use to access approved local models, with stable credentials and tool-call transport.

Emergency Isolation Mode

Protect the core while keeping operations available.

Emergency Isolation Mode blocks Application inference at the Product edge while Console, identity, audit, hardware monitoring, backup and recovery remain available.

Lifecycle and recovery

Change the stack with a recovery path.

Controlled updates use preflight checks, local snapshots, health validation and documented rollback procedures.

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 Documented API + internal-use rights to first-party Product source
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 staffing, configuring and operating every layer internally with a managed private inference service.

See the full breakdown →

Building it in-house means staffing, integrating and operating every layer. Partnering with us means starting from a managed operational foundation.

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

Scoped around your operating requirements.

A quote follows discovery and reflects your infrastructure footprint, inference estate, availability, update policy, recovery scope and support access.

See how service scope is assessed →
Service scope
Infrastructure & models
Workload sizing, model selection, capacity planning and distributor-backed Supermicro procurement.
Managed operations
Identity, model access, observability, update policy, backup, recovery and capacity reviews.
Application handoff
API credentials, approved model access, a documented API contract and a responsibility handoff for customer-owned applications.
Managed private inference
Configuration · maintenance · optimization

A supported inference environment operated inside infrastructure you control.

Supermicro infrastructure
Sized · distributor-backed · customer-controlled

Enterprise infrastructure sized by LLM Machines and fulfilled through a distributor-backed procurement channel.

Outcome-led consulting
Separate SOW · measurable acceptance · handoff

Integrate customer-owned applications only through a bounded engagement with explicit post-handoff ownership.

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, workload sizing, infrastructure design and distributor-backed procurement planning.

01–02
Installation
Hardware & first boot

Receive, rack, power, verify the signed release manifest and complete appliance self-tests.

03–05
Platform configuration
Federate & integrate

Identity federation, inference policies, API credentials and operational Console configuration.

06–08
Validation & handoff
Validate & partner

API handoff, isolation, backup and recovery tests, operational acceptance and ongoing maintenance.

09 — Timing

Why now.

The market is reaching a tipping point.

10 — Company

European, by design.

A small technical team configuring and operating managed private inference for European enterprises. Bootstrapped on conviction.

About the company →
Mission

To make sovereign AI practical through infrastructure customers control and an inference service they can operate with confidence.

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 does LLM Machines operate?

LLM Machines configures, maintains, monitors, updates and optimizes private inference inside customer-controlled infrastructure, including model serving, identity, observability, backup and recovery.

Does company data leave our network?

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

How do applications access local models?

Connect through the documented Models and Chat Completions endpoints, with application credentials, model discovery and tool-call message transport.

How long does deployment take?

Deployment follows a documented sequence covering discovery, sizing, distributor-backed procurement, installation, inference configuration, API handoff and operational acceptance.

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

Bring managed private inference in-house.

We size, configure and operate private inference inside your perimeter. Your applications connect through the documented API. Nothing leaves your infrastructure.

Talk to sales → Explore the technology