Warum europäische Mittelständler ihre eigene KI-Infrastruktur brauchen

Mid-sized European companies — 20 to 500 employees — face an uncomfortable gap. Enterprise AI platforms cost €200K+ and take six months to deploy. Consumer tools like ChatGPT can't see your internal files, can't connect to DATEV, and process data on US servers.

The Problem with Generic AI Tools

Your tax advisory firm has thousands of regulatory documents. Your recruitment agency processes hundreds of candidate profiles weekly. The knowledge is there — trapped in PDFs, Word files, and email attachments.

Generic AI tools can't help because they:

• Can't access your documents. ChatGPT doesn't see your internal file server.
• Aren't GDPR-compliant. Data leaves the EU the moment you paste it into a prompt.
• Don't connect to your tools. No DATEV push, no Salesforce sync, no Slack integration.
• Don't learn your business. Every conversation starts from zero.

What a Dedicated AI System Looks Like

A dedicated system means your own AI instance — running on European servers, configured for your document types, connected to your tools.

For a tax advisor, this means:

• Upload your regulatory library once. Your team queries it in plain German.
• Incoming invoices get extracted and pushed to DATEV — automatically.
• A Slack bot so staff can ask questions from anywhere in the firm.

For a recruiter, this means:

• Job boards scraped every morning at 6 AM.
• Candidate profiles extracted and pushed to your CRM before your team starts work.
• A Slack summary waiting with the day's results.

Why European Infrastructure Matters

GDPR compliance isn't a checkbox you tick after building on AWS. It's an architectural decision you make on day one.

When your AI system runs on Hetzner servers in Germany, with Mistral AI (hosted in France) as the default LLM, and PostgreSQL with per-client schema isolation — GDPR compliance is structural, not contractual.

No data crosses the Atlantic. Not because of a policy document, but because of how the system is built.

The Managed Service Difference

The gap we fill isn't just technology — it's service. Mid-market companies don't have AI engineering teams. They need someone to deploy the system, configure it for their workflows, monitor quality, and improve it over time.

That's what managed AI deployment means: you use it, we maintain it.

---

Interested in seeing how a dedicated AI system works for your industry? Book a 30-minute demo — we'll show you the system running on sample data from your vertical.