AI that delivers measurable benefits — not toys.
We are introducing AI where it takes over real work: at
Text generation, image analysis, knowledge search and voice assistants.
GDPR-compliant, with provider selection based on data protection rather than hype.
What does AI mean in business?
By "AI" we specifically mean Large Language Models (LLMs such as
GPT-5 or Claude 4.5), vision models (image analysis,
Document understanding) and speech models (speech-to-text and
Conversely, they do not replace people, but rather relieve them of burdens.
Repetitive knowledge work: sorting, summarizing,
Classify, generate. A clean system is always a prerequisite.
Data basis — AI strengthens existing structures, it replaces
They don't.
Eight productive areas of application
We don't recommend anything that only works in demos. These fields
We have implemented this multiple times and deliver measurable savings.
- Knowledge search in the enterprise data repository (RAG) — ask employees, AI answers from your own documents.
- Text generation — offers, emails, product descriptions at the touch of a button, in the brand's tone.
- Image analysis — evidence recognition, damage images, quality control.
- Voice assistants — answering the phone, booking appointments, transcription.
- Classification — Automatically prioritize requests by urgency or topic.
- AI-powered accessibility — automatically generate alt text, plain language, and subtitles.
- Code and SQL generation — build reports and small tools significantly faster.
- Data extraction from PDFs and unstructured emails.
AI and GDPR — how do they fit together?
Data privacy is not an obstacle to the introduction of AI, but rather an advantage.
Design decision. We separate three stages: EU-hosted
AI (Models at IONOS, Azure EU, Hetzner — data remains
in the EU), local models (Open-source models)
like Llama, Qwen on self-managed infrastructure — data leaves the
(not house) and curated US providers (OpenAI,
Anthropic with a data processing agreement, zero retention,
(No training based on your data). Which level is suitable depends on...
Data classification for your use case — we'll clarify that in the
Initial consultation.
Which AI providers do we use?
We are vendor-neutral. Selection follows three criteria: suitability.
For the use case, data privacy profile, total costs. None.
Commissions, no upfront commitment. Where possible, we operate
AI models on
self-managed infrastructure in Germany
— so your data doesn't leave the house, not even for
Inference.
Note: AI only works reliably when the digital foundation is in place.
stands — clean data comes from
Process automation,
clean structures made of
Accessibility.
- OpenAI (GPT-5)
Strongest overall model. US provider, with an enterprise contract that complies with GDPR (zero retention, EU data residence). - Anthropic (Claude 4.5)
Market leader in code generation and long texts. US provider, GDPR compliant. - IONOS AI Model Hub
Open-source models (Llama, Mistral) on a German cloud — good for sensitive data. - Local models (Ollama / vLLM)
Completely in-house, without cloud dependency. Maximum data protection, higher hardware costs. - Azure OpenAI Service (EU)
OpenAI models in the European Microsoft cloud — a popular choice for larger companies.
Four steps to productive AI
Needs analysis
We examine which tasks are suitable for AI and which are not — together, without tech jargon.
Provider selection & security
We select the model, hosting, and data processing agreement (DPA) based on the data classification of your use case.
Build a pilot
We will quickly bring a first productive workflow live — with test data, then live data.
Team training
Team training plus cheat sheet so your team can use AI independently and understand its limitations.
Quick answers about AI implementation
Will our data be used to train the AI?
No — we exclusively use tariffs/contracts that explicitly exclude training on customer data (zero retention for OpenAI/Anthropic Enterprise, local models automatically). This is stated in the data processing agreement.
Do we need our own AI hardware?
For 90% of the use cases, no — we use cloud-based AI. Owning our own hardware only makes sense for very high volumes or maximum data protection requirements (e.g., healthcare or public administration).
What if the AI outputs nonsense (hallucinations)?
We're integrating response verification into the workflow: source evidence for RAG, rule-based validation, and human approval stages for critical tasks. AI is a tool—not an oracle.
How much does it cost to implement AI?
This depends heavily on the use case—complexity, data basis, choice between cloud and on-premises models. We don't provide a fixed price, but rather a specific estimate after the initial consultation. Consulting and concept development services are often eligible for BAFA funding.
Do employees need to fear for their jobs?
Our experience: No — routine work disappears, skilled work increases. We actively train the team so that AI is understood as a tool, not a threat.
Which use cases are suitable for AI — and which are not?
15-minute initial consultation — we give an honest assessment, no hype.
Further information:
Previous stage: Automation ·
BAFA funding ·
Performance overview
