Private AI Infrastructure for the Mittelstand

Your company needs AI.
Your data doesn't need to
leave the building.

We deploy private AI infrastructure directly inside your company, powered by modern open models, connected to your internal knowledge, and tailored to your business. Your employees get a ChatGPT-class assistant that also understands your documents and processes. No public AI cloud. No confidential data sent to third parties. Your AI runs where your data lives.

Book a Private AI Assessment See how it works

Private by design

🔒
Your data never leaves your network
No public AI cloud, no third-party training, no exceptions.
📁
Answers from your own documents
Every response points back to the internal source it came from.
🧰
The stack is ours to run
You get a working assistant. We take care of everything behind it.
COMPANY AI ASSISTANT ON-PREM
🧑‍💼
What is our procedure for handling a customer return?
🤖
According to procedure QP-14, section 3.2, a return is logged in the ERP under an RMA number, inspected within 3 working days, and either credited or replaced. Goods above 1,000 EUR require quality sign-off before a credit note is issued.
Sources
QP-14_Return_Handling_v4.pdf, p.3
ERP wiki: "Creating an RMA"
🧑‍💼
Draft an email to the customer confirming the replacement and the timeline.
🤖
Draft ready. It references the RMA number, a 3 day inspection window, and a replacement shipment within 10 working days, in your standard support tone. Nothing in this exchange left your network.
100%
On-Premise
Zero
Data to Public AI
Privacy
By Architecture
Air-Gap
Capable
The Problem

AI is already inside your company

The question is not whether your team uses AI. They already do. The question is where your data goes and whether the AI actually knows your business.

🕵️

Employees already use AI

Staff paste specs, contracts, code and customer data into ChatGPT, Gemini and Copilot. Most companies have no rules and no visibility. You can forbid it, or you can give them a controlled environment.

🔓

Confidential data leaves the building

Sending personal or confidential information to external AI services creates data protection, contractual and governance exposure. The cleanest fix is not to send it in the first place.

🧩

Generic AI does not know your company

ChatGPT has never read your procedures, your product documentation, your quality manuals or your ERP. It cannot answer "which spec applies to machine 7" without your data behind it.

🌍

Dependence on foreign AI clouds

API prices change, terms change, services go down, and the infrastructure sits outside your control. Digital sovereignty is now a board-level concern in Germany, not a slogan.

How It Works

Your data. Your infrastructure. Your AI.

Everything runs on hardware you control. Documents are indexed locally, inference happens locally, and answers come back with the source attached. By default, nothing is sent to an external model.

Your data ▸
Your infrastructure ▸
Your AI ▸
Your employees
YOUR COMPANY NETWORK
DATA SOURCES
📁SharePoint & file servers
📄PDFs & Office documents
📚Wiki / Confluence
🗃️ERP / CRM
💾Databases & Git
BunkerAI Cluster
BunkerAI Cluster
Local models + RAG
On-premise · No cloud
YOUR TEAM
🖥️Web AI portal
💬Chat assistant
⚙️AI agents & API
EXTERNAL AI CLOUD
✕
DATA LEAVES NETWORK
✕
PERMISSION-AWARE
✓
AIR-GAP READY
✓
Applications

Packaged use cases, not "AI in general"

Each deployment starts from a concrete outcome your team can see on day one.

📚

Company Knowledge AI

Ask questions across manuals, PDFs, SharePoint and the internal wiki. Answers come back with citations to the exact document and page.

Cites the source
every answer is traceable
💬

Internal AI Assistant

A private CompanyGPT for writing, summarising, translating, analysing documents and drafting reports. Same convenience as public tools, inside your walls.

ChatGPT-class
running on your hardware
🛠️

Engineering Copilot

Connect technical manuals, specifications, standards, CAD documentation and Git. Ask which component fits a requirement or what changed between two revisions.

Spec-aware
grounded in your engineering data
🏭

AI over Operational Data

For manufacturers: reason over production data, logs and sensor history. "Why did line 3 raise three temperature alarms yesterday?" Backed by our BunkerM industrial experience.

MES / historian
connected read access
🤝

AI Agents

Move from answers to actions: create a maintenance ticket, find all invoices above a threshold from a supplier and summarise discrepancies, route the result to the right person.

Human approval
before any write action
🔒 PRIVACY BY ARCHITECTURE

Designed for controlled data processing

What we provide is an architecture where the sensitive parts stay under your control by default: local inference, local storage, local vector database, and no outbound AI traffic unless you enable it.

On-premise inference

Models run inside your network. Documents and prompts are processed on hardware you own. No third-party AI processor is involved by default.

Permission-aware knowledge

The retrieval layer respects your existing access rights, so an employee only sees answers built from documents they are allowed to read.

RBAC, SSO and audit logs

Single sign-on, role-based access and a full log of every query and action. Optional air-gapped deployment with signed offline updates.

Deployment

Three layers, deployed on your premises

Standardised building blocks, so 80 percent of every deployment is the same and only the last 20 percent is tailored to you.

1

Private AI server

A GPU server installed in your rack, with Linux, container runtime, model serving, monitoring, backups and authentication. You own the hardware.

▸ GPU infrastructure▸ Model serving▸ Monitoring & backups▸ SSO / RBAC
2

Your knowledge layer

A permission-aware retrieval layer over your documents and systems: SharePoint, file servers, wikis, databases, Git, ERP and CRM. Kept in sync as content changes.

▸ Document ingestion▸ Local vector database▸ Access-right aware▸ Source citations
3

AI applications for your team

A web AI portal, chat assistant, document analysis and agents. Multiple models behind one interface: a general model, a reasoning model, embeddings, vision and speech.

▸ Web AI portal▸ Agents & workflows▸ Multiple models▸ API for integrations
100%
On-Premise Deployment
Zero
External Inference by Default
80/20
Standardised / Tailored
Packages

Sized to your organization

From a first private assistant in production to a company-wide platform spanning multiple departments and systems.

AI Starter

Smaller teams getting a first private assistant in production.

  • ▸AI server with GPU
  • ▸Local model + web interface
  • ▸Basic RAG over key documents
  • ▸SSO and basic monitoring
  • ▸Deployment and team onboarding
Most common

Private AI Business

The main product for a 50 to 500 person company.

  • ▸Multi-model GPU infrastructure
  • ▸Full document ingestion + RAG
  • ▸SSO, RBAC, monitoring, backups
  • ▸Custom integrations and AI agents
  • ▸Training and rollout support

Private AI Enterprise

Multiple departments, high availability, deeper integration.

  • ▸Multiple GPU servers, HA
  • ▸Kubernetes and advanced networking
  • ▸ERP / MES / CRM integration
  • ▸Agents, computer vision, voice
  • ▸Disaster recovery and SLA

Scope and pricing are worked out together on a direct call.

Why Now

The market has moved from "if" to "how"

Adoption is rising fast while governance and data questions stay unsolved. That gap is the opportunity.

41%
already using AI
of German companies with 20+ employees, with another 48% planning or discussing it (Bitkom, 2026).
~780k
Mittelstand companies
around 20% of the German Mittelstand now use AI, higher among firms above 50 employees (KfW, 2026).
68%
want less dependence
of Germans surveyed feel the country is too dependent on the US and China for AI (Bitkom).
Why Us

We have already built self-hosted AI infrastructure

We spent years building industrial IoT and on-premise AI systems. BunkerM is our open-source proof: a self-hosted platform that runs a local LLM against live operational infrastructure, with no cloud dependency. We are bringing the same philosophy to company-wide AI: infrastructure and integration first, models kept replaceable.

Explore BunkerM (open source)

Book a Private AI Assessment

A short, fixed-scope engagement: we look at your data, use cases, infrastructure and security, then deploy a limited proof of concept. If you go to production, part of the fee is credited against the deployment.

Book the assessment Talk to us

sales@bunkerai.dev