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On-Premise AI for Business

On-premise AI for
business —
when your data
doesn't belong in
someone else's hands.

Pixelschnitzel sets up AI systems that, on request, run inside your own infrastructure or in a controlled environment — for more privacy and control.

Privacy-focused · can run inside your own network on request
GDPR‑compliant
Hosted in Germany
BVMID‑certified AI expert
10,000+ active users
75+ projects since 2023
In time / in budget
A personal point of contact
What on-premise AI is really about

Many companies want to use AI,
but not at any cost.

·
Cloud AI is powerful, but every request leaves your own network. For sensitive data, that's not an option in many organisations.
·
Data protection, trade secrets and works councils expect clear, traceable rules. A standard chatbot rarely meets that bar.
·
Token-based billing makes costs hard to plan. We work out up front what makes sense on-premise and what doesn't.
·
Cloud AI rarely comes without conditions. On-premise AI is an alternative where data sovereignty and control take priority.
What does on-premise AI mean?

AI that doesn't necessarily
have to run in the public cloud.

On-premise AI means AI systems don't have to run exclusively through public cloud services. Depending on the model, the security requirements and the area of use, they can run locally or in a private environment.

Which operating model fits is something we work out in a sober analysis, not in a sales pitch.

Local
Runs inside your own network, on your own hardware.
Private
In a dedicated environment, on request with a German host.
Hybrid
Sensitive data locally, less critical steps optionally in the cloud.
Sovereign
Open-source models, documented architecture, no vendor lock-in.
Cloud vs. on-premise

The direct comparison.
Without the marketing filter.

Criterion
Cloud AIOpenAI, Google, AWS …
On-premise AIOn-premise · your own hardware
Data flow
Through provider data centres
Stays within the defined boundary
Data-protection assessment
Data processing agreement + setup needed
Less data transfer required
Trade secrets
Secured by contract
Controllable at the architecture level
Cost model
Variable · scales with usage
Plannable · investment + maintenance
Internet dependency
High · no connection, no operation
Low · runs even on your internal network
Response time
Depends on API + internet
Can be very low depending on hardware
Vendor lock-in
High · API migration is costly
Low · open-source models usable
Model control
Provider controls versioning
You choose the model and the update timing

On-premise AI isn't automatically better, it's different. We help you make the decision with a clear head.

Rack of servers in a data centre
Your infrastructure, your AI

Hardware, models and applications
tuned to each other, and ready to run in-house.

Typical use cases

Where on-premise AI
makes a real difference.

Law firms & notaries

Client information never leaves the building.

Draft pleadings, analyse contracts — with an AI that uses internal documents without them flowing into public services. Professional duties stay part of the architecture.

Professional confidentiality and AI use have to fit together technically, not just legally.
Hospitals & practices

Process patient data with privacy in mind.

Draft doctor's letters, summarise findings — on request in a controlled environment. What may be processed is something we clarify with your data protection officer.

AI as relief, but only in the way data protection and the duty of care allow.
🏭
SMEs & industry

Protect engineering and sales knowledge.

Requirement specs, engineering data and supplier contracts made usable locally — without internal information flowing uncontrolled into external systems. Integration into ERP and document systems is part of the concept.

Decades of accumulated knowledge belong where it was created, inside the company.
🏛
Public authorities & administration

Make administrative knowledge usable, under control.

File research, draft reports, internal knowledge assistants — on your own or dedicated infrastructure, with clear roles and audit trails.

AI in public administration needs clear limits of use, and an operating model that IT can support.
Research & development

Keep IP-sensitive content in-house.

Test series, patent research — on-premise AI makes content accessible without research results ending up in external training data.

Anyone who researches for years should decide for themselves who gets to read the result.
🛡
Insurance & finance

Regulated processes, assisted under control.

Claims reports, dossiers, internal research — AI support in an environment that fits regulatory requirements. We clarify the operating model on a project-by-project basis.

AI in regulated industries isn't a tool purchase, it's an architecture question.

For the public sector we have a dedicated page: on-premise AI in public administration.

Why on-premise AI?

Six reasons
that matter to decision-makers.

01
More control over your data

You decide where your data is processed and which data goes into the AI in the first place.

02
Less platform dependency

Open-source models, documented interfaces, no vendor lock-in to a US hyperscaler.

03
Tailored adaptation

Models and applications can be adapted to your language, documents and processes.

04
Integration into existing systems

Connection to ERP, DMS, CRM and mail systems — not as an island, but as part of your working world.

05
Usable for internal content

Your own documents, policies and processes become the data basis, not general knowledge from the internet.

06
Easy to justify

To data protection, IT security and management you can explain exactly where which data ends up — and where it doesn't.

How we build it

Three layers.
One sovereign solution.

01
Layer / Hardware

Your own infrastructure.

We recommend, procure and install — from a workstation server to a GPU rack, alternatively dedicated with a German host.

NVIDIA RTX 6000 NVIDIA H100 AMD MI300 EPYC / Xeon 512 GB RAM GDPR host (DE)
02
Layer / Models

Open-source models at a high level.

We pick the right model for your use case and swap it out when a better one comes along — without API lock-in.

Llama 3.3 70B Qwen 2.5 Mistral Large Gemma 3 DeepSeek Phi-4
03
Layer / Applications

Integrated into your working world.

Chat interface, RAG over your knowledge base, connection to ERP, DMS or CRM — we build the interfaces, cleanly documented.

Internal chat UI RAG pipeline Qdrant / Weaviate SAP connector Outlook / Teams REST + MCP
Cost comparison: cloud vs. on-premise

When on-premise AI
pays off.

Every project is individual, which is why there are no list prices; hardware prices are only ever a same-day snapshot. With us, AI projects start from €10,000. The actual calculation is project-specific: on request we work through the business case with ROI, cloud vs. on-premise, for your scenario — after the 30-minute initial call, with no obligation.

The first 90 days

From first call
to a productive system.

Days 1–14 · Discovery

Audit & use case

2 workshop days on site

Which data can go in, which use cases are worthwhile? We prioritise by effort × benefit and tell you honestly what isn't worth it.

Days 15–45 · Setup

Hardware & models

Delivery · installation · configuration

Set up the server, install the models, configure the RAG pipeline. Security audit in coordination with your IT.

Days 46–75 · Integration

Connections & RAG

Interfaces to your systems

ERP, DMS, mail, knowledge base — we connect what belongs together. The AI answers about your company.

Days 76–90 · Rollout

Training & go-live

Hands-on training · monitoring

We train management, departments and IT. Monitoring is in place, maintenance is running — you're productive and independent.

What you receive at the end

Concrete. Documented.
Usable in your business.

01
A working system

An AI system on your infrastructure, installed, configured and adapted to your use cases.

02
Access to your own knowledge

A searchable knowledge base from internal documents and policies, on request with source references in the answers.

03
Architecture and operations docs

Clear documentation for IT, data protection and management — so your organisation can carry on its own what we've built.

04
Training & guardrails

An introduction for users and departments, including clear guidance on what the AI may and may not do.

Why Pixelschnitzel?

Consulting, software development and AI
from a single source
so concept and execution fit together.

  • A personal point of contact. Florian Brosig in person, from the first call to well after go-live.
  • Experience with custom business software. Over 10,000 active users in existing systems we have built and supported.
  • BVMID-certified AI expert. Certified AI expertise, embedded in an association for mid-sized businesses.
  • From the Ruhr region, deliverable across Germany. Based in Herne, short distances within NRW, projects nationwide.
Frequently asked questions

The questions we
hear most often.

Is on-premise AI really at ChatGPT level?
For typical business tasks: yes. Llama 3.3 70B, Qwen 2.5 or Mistral Large deliver comparable results to GPT-4. For top-end use cases there are hybrid setups where sensitive data stays local.
What does getting started actually cost?
Every project is individual, which is why there are no list prices. With us, AI projects start from €10,000 — we provide a concrete figure after the initial call, on request with a business case and ROI.
We don't have any server hardware. What now?
Two options: we procure and install on your premises, or you use hardware with a German host (e.g. Hetzner, IONOS) and we configure it remotely. How to set that up with privacy in mind is something we clarify with your IT.
What happens when the model becomes outdated?
Switching models is routine, no new server needed. Roughly every 6–12 months relevant open-source releases appear; we update them as part of maintenance. You decide when to switch.
How fast does the system respond?
With the right hardware choice: 200–800 ms for the first tokens, a full answer usually in 2–6 seconds. The cloud is often slower, because internet latency is added.
We're a data processor for our clients. Does this fit?
On-premise AI can make sense here, because the data flow can be controlled more tightly. Whether that's enough depends on the contract and data protection concept. On request we support the related technical documentation.
We already use Microsoft Copilot. Isn't this the same thing?
No. Copilot runs on Microsoft infrastructure, so data processing sits with the provider. On-premise AI runs in an environment that you control. Which variant fits depends on the use case and the protection requirements.
Who is responsible when the AI makes mistakes?
As with any software, there's responsibility for selection, operation and use. We build in safety nets: source references in RAG answers, documented limits of use. AI governance is part of every project.
Pricing & investment

Clearly calculated.
Honestly reasoned.

Every project is calculated individually — the figures below show the budget at which a project with us starts. We make the numbers transparent before any investment, with a business case and ROI on request.

Software
from €5,000

Built around your business processes — from a single tool to a complete platform. What it costs and what it saves, we'll tell you after the first call.

Artificial Intelligence
from €10,000

RAG, chatbots, on-premise models, OCR, mail classification. We start where AI measurably takes work off your hands — not where it merely sounds good.

Hardware for on-premise AI
Snapshot

Server and GPU prices are highly volatile right now. We provide an up-to-date calculation before you approve any order.

In time / in budget

We calculate before we build — and deliver in time / in budget. Lean structures without a sales machine or corporate overhead: you pay for development, not administration.

Business case & ROI

On request, we calculate a complete business case with a concrete ROI before the project starts. So you decide on impact, not on price.

Our promise

If a problem is technically solvable, we commit. If it isn't — or simply doesn't pay off — we'll tell you that too: with a reason, and usually with a pragmatic alternative.

Sovereign.
Initial call about on-premise AI

Want to use AI
without losing control
over your data?

In 30 minutes we'll work out which use cases make sense for you and what a first, manageable step could look like. No slides, no sales pitch.