Looking for AI consulting without the bullshit?
No vendor demos, no AI roadmaps for Q3. We are pragmatic, direct and will tell you when a solution simply does not make sense.
A Copilot license is not an AI strategy.
The problem isn't that companies have too little AI. The problem is that nobody knows where to start — and in the meantime, shadow IT grows, data gets duplicated, and tools pile up that nobody maintains. In a few years, the first AI consolidation projects will kick off. We've seen this film before.
Not starting is still not an option. While you wait, your competitors are already running working solutions.
Are they perfect? No.
Are they better than nothing? Absolutely.
Which one are you?
None of them is a strategy. All of them are avoidable.
The non-starters
Data privacy concerns, uncertainty, too many open questions. Understandable — but meanwhile, the competition is building.
The risk: Those who don't start now will be catching up in two years under twice the pressure.
The action-takers
Copilot here, AI module there, ChatGPT wrapper somewhere else. High costs, little impact. At least they're "doing something".
The risk: Uncontrolled shadow IT and costs with no measurable value.
The overbuilders
Custom AI systems that talk to everything — but nobody understands them, nobody maintains them, nobody knows what they cost.
The risk: Technical debt 2.0 — this time with an AI label on it.
No AI deck. No roadmap for Q3. Just the next concrete step!
Our AI consulting does not start with a slide deck. We look at your entire environment: tools, departments, data foundation, already running solutions. We identify the no-brainers, where can AI take over work tomorrow that nobody wants to do manually today?
And we show you where shadow IT is already growing, before it becomes a problem.
That's exactly what makes us the AI consulting partner that actually works for mid-market companies, no enterprise playbook, just solutions that fit your size.
A mini glossary of AI terms
AI terms come at you from everywhere, in meetings, in LinkedIn posts, in every second tool update. We figured we'd collect the important ones in one place, for anyone who's always wanted to know what RAG actually means. If you already know all of this, feel free to keep scrolling.
Prompt: The instruction or question a person gives to the AI. The skill of writing good prompts is called prompt engineering.
AI model: The trained system itself, which has learned patterns in data to generate answers, text, or decisions from them. Not magic, just a lot of math.
LLM: The actual language model behind it, GPT, Claude, Gemini, and so on. The foundation your AI solution is built on, but only the foundation, not the finished solution.
Generative AI (GenAI): The umbrella term for AI that can create new content on its own, text, images, music, or code, as opposed to classic AI, which only analyzes or sorts.
AI agent: A model that doesn't just answer, but also acts, calls tools, plans steps, and completes tasks, without you having to be present for every single click.
RAG: Instead of an AI answering purely from its training, it first retrieves relevant information from your own documents and builds the answer on that. That's why it hallucinates less and actually knows your company, not just the open internet.
Context window: How much a model can remember before it forgets the start of a conversation. Small means you have to keep repeating yourself. Large means it keeps track, even across long documents.
Token: The smallest unit of text an AI breaks language into before processing it, sometimes a whole word, sometimes just a syllable. Important for you, more tokens usually means more cost, that's the billing unit behind most AI tools.
Hallucination: A phenomenon where an AI generates false information, but states it so confidently and convincingly that it sounds like the absolute truth.
Fine-tuning: The process of taking an already trained AI model and training it further on specific data, for example your company's internal documents or industry literature, to optimize it for a particular purpose.
Typical challenges in AI consulting
AI Hackathon – Build Real AI Solutions in 48 Hours
AI hackathon: why 48 hours of building together beats 12 months of AI strategy, and how we work with your team to ship real solutions fast.
AI Automation for Mid-Sized Businesses: The Right Process
AI automation: why most projects target the wrong process, how to find the right one, and what a realistic first step actually looks like.
AI Basics: What Needs to Be Right Before AI Makes Sense
AI basics that need to be right before AI makes sense: clean data, clear processes, defined ownership. Why AI rollouts fail without them.
AI Strategy Consulting – From Slide Deck to Real Results
AI strategy consulting: what a real AI strategy looks like versus a slide deck, and why the most annoying use case is the best starting point.
AI Tools Comparison: ChatGPT, Claude and Gemini
AI tools overview: ChatGPT, Claude and Gemini compared, and why choosing the tool is not the most important question for your AI project.
Vibe Coding: 80% Hype. 20% Fight.
Vibe coding: the first 80 percent build fast. The last 20 percent are a real fight. What the hype videos do not show, and when vibe coding actually works.
Frequently asked questions
Potentially everything. What AI won't change as quickly: areas where people work with atoms, so physical labour, craftsmanship, direct human interaction. Everything digital is already transforming, but even that is still in its early stages. Those who are in it now have the chance to shape it rather than just watch.
All three are large language models and more similar under the hood than their marketing departments would admit. In practice, choosing the LLM is not the critical question. Far more important is: what problems do we want to solve internally with it? AI is a lot more than a chat interface. If you start with the tool question, you're asking the wrong question.
Both can be true depending on where you are starting from. The AI features in your existing systems are often a good low-effort entry point. The problem arises when you mistake that for a strategy. A real AI strategy looks at the full picture: which processes can be automated, what data do you have, and where is the biggest lever?
Yes, absolutely. Just get in touch, that's exactly what we're here for.
Yesterday would have been ideal. The market moves fast, yes, but gathering experience matters more than waiting for tomorrow's supposedly best solution. Those who wait will be catching up in two years under twice the pressure.
It depends on the scope. A first assessment with concrete recommendations takes us two to three weeks. First working solutions in another four to eight weeks. We don't work in quarters.
Yes. AI consulting is simply the English term for the same thing. AI stands for Artificial Intelligence, KI is German for the same.
Yes, especially. We don't build enterprise solutions that need three departments to understand. Our AI consulting is built exactly for mid-market and SME companies, pragmatic, without unnecessary complexity.
Not in Hamburg? No problem.
Most of our work runs remote anyway. If you happen to be in Hamburg, we're happy to meet in person too, no fixed rhythm.
NO DISCOVERY THEATER.
Tell us where you stand — we'll tell you in one conversation where the no-brainer is.