AI Solutions & Integration for Qo'noS
AI is more than a ChatGPT demo. We embed large language models, RAG systems and computer-vision models directly into your business applications — GDPR-compliant, production-ready and with measurable ROI.
AI use cases we deliver
Not every problem needs AI. But when your team spends hours every day on routine work, the case for it is almost always there.
- Knowledge base (RAG)
Staff ask questions in plain language — the AI answers from internal documents with citations. - Document processing
Invoices, delivery notes and contracts read out automatically and pushed into ERP systems. - Email classification
Categorise and prioritise incoming mail, draft responses to standard requests. - Computer vision
Image classification, quality control, defect detection on the production floor. - Chat & voice bots
Customer-support bots with live data access into CRM and ERP. - Content generation
Product descriptions, SEO copy, translations — with brand-voice guard-rails.
The AI models we use
We're model-agnostic and choose the right tool per project.
- OpenAI GPT-4, GPT-4o, GPT-5 — highest general capability, broadest availability
- Anthropic Claude (Opus, Sonnet, Haiku) — strong on code and long context
- Mistral & LLaMA — open-source for on-premise deployment
- Aleph Alpha — purely European model for highest data sovereignty
- Specialist models: Whisper (STT), FLUX/Stable Diffusion (image), BERT variants (classification)
GDPR & data protection in AI projects
A common show-stopper for AI projects is GDPR. We know the pitfalls and ship architectures that even an enterprise data-protection officer signs off on.
- EU hosting via Azure OpenAI (Frankfurt), AWS Bedrock (Frankfurt) or fully on-premise
- Data Processing Agreements (DPA) reviewed and put in place
- PII filtering before any external model is called
- Audit logs covering every single model call
- When required: fully local models with no internet connection
From PoC to production AI
Many AI initiatives stall on the jump from proof-of-concept to production. Realities show up that no tutorial mentions:
- Hallucinations: LLMs invent facts — contained with RAG, citation forcing and output validation.
- Latency & cost: Streaming, caching, model routing by complexity.
- Prompt injection & jailbreaks: Security layer and input sanitisation.
- Evaluation: Test sets, automated evals, human-in-the-loop.
- Drift: Model updates change behaviour — continuous monitoring instead of set-and-forget.
Frequently asked questions
Is AI worth it for a mid-size business?
When your team spends hours a week on routine work — yes. A well-built AI application typically pays for itself in 6–18 months.
Do we have to send our data to OpenAI or Anthropic?
No. The same models run via Azure OpenAI in Frankfurt or AWS Bedrock in the EU. Alternatively, open-source models on your own hardware.
How much does an AI project cost?
It depends on the use case — a focused integration is small; a platform-grade solution larger. After a short workshop we put together a transparent estimate including ongoing API costs.
How do we start an AI project?
With a one-day workshop where we jointly prioritise use cases. Then we build a first production-grade prototype within 4–8 weeks.
