Does everyone really need to become a prompt ninja? No. AI should be as easy to use in everyday work as a finished Excel sheet – experts build it, your team uses it.
That is exactly what I deliver: strategically sound and technically robust – including the honest question of whether you need AI at all yet. Not everyone needs to understand the formulas behind Excel. The same should apply to AI. How I structure such engagements is described under Process.
"Everybody uses Excel, but not everybody understands the formulas. The same should apply to AI as well."
— Stephen KunstmannWhat does AI actually deliver for you – and what does it not?
C-level workshops on use cases, prioritization, risk, governance and measurable business goals. I start with an expectations check-in: clarify what you need upfront rather than discovering afterwards that we worked on the wrong topic.
The prompt stays in the background – like an Excel formula.
Deep prompting capabilities for reliable outcomes in analysis, text, code and assistant workflows. Your domain team uses the template; I make sure it holds up – including quality assurance.
From prototype to workflows that work in daily operations.
Design and implementation using LLM, LRM, foundation models, RAG, MCP, APIs and production-oriented toolchains. Multiple models per use case, flexibly interchangeable – with proprietary data integrated via RAG, APIs and MCP.
Good AI solutions must be easy to use in daily operations, without prompt overload for domain teams. Makes sense, right?
In workshops I rely on pairing: domain experts and technical specialists work together. The domain side owns quality assurance – because AI answers with confidence even when it is wrong. Practical examples are on References.
"AI does the heavy lifting. You keep quality assurance."
— Stephen KunstmannAn everyday image first, then the technical term – as in my workshops.
Library and cut-off: A language model has a knowledge state with a fixed date – like a library that does not instantly hold every new book. RAG is the index: I pull your files selectively, not everything from memory. PDF vs. Word: Data quality decides whether AI works reliably – poor sources produce poor answers, regardless of prompt quality.
I am a practitioner – I build, test and implement, rather than only showing slides.
Which format fits you? See my Services – or we clarify it directly in a call.
Clarify AI potential in an initial callThe questions that come up in almost every initial call – answered here in advance.
No. Just as not everyone needs to build pivot tables to use Excel. The experts build the template, your team uses it. The prompt stays in the background – like the Excel formula.
Cursor, Anthropic, Gemini, OpenAI/Azure GPT, Mistral, Perplexity – the right model for each use case. I deliberately stay flexible: the market moves so fast that a fixed commitment to one model quickly becomes a brake.
Via RAG, APIs and MCP – controlled and traceable. More important than the technology is data quality: poor sources produce poor answers, no matter how good the prompt is.
It does – and with full confidence. That is why my rule is: AI does the heavy lifting, your domain team keeps quality assurance.
Your question is not covered? Then ask me directly.
