We find the use cases that pay off for you — and build them: from potential analysis to AI agents in daily use. With an honest answer whenever AI is not the solution.
AI is only valuable when it solves a real problem. We help you cut through the hype and ship AI features that genuinely save time, reduce cost and create new capabilities — built responsibly and integrated into your products.
Everyone is talking about AI, but most projects stall in endless demos. You need use cases that pay off — not experiments — and a partner who knows where AI genuinely helps and where it does not.
Revolutionise how you work with practical AI — from intelligent automation to assistants and insights.
Technologies we use
What changes in practice — and where the human stays in charge: your team approves, it no longer processes.
Every case is read, sorted and passed on by hand — again every single day.
The agent reads, decides and uses your systems — your team only approves.
Three AI agents work in our own team every day — research, content and quality assurance. What runs reliably for us, we build for your processes too.
Recording
This is how our AI assistant walks through five questions — and then sends you a recommendation that fits your situation.
We advise mid-sized companies across the Munich region on practical AI — and build it with our own hands. You get a fixed contact person, in-person workshops where they help, and honest answers on where AI pays off and where it does not.
Wolfe W. Diener
Founder & CEO
Baaderstraße 40, 80469 München
Personal reply within one business day.
References from the region
AI can lift efficiency across many parts of your business. The hard question is not whether, but where to start. That is exactly what we answer — with a prioritised list of concrete use cases from your own working day.
About half a day on site — we come to you.
The mix is what matters: the best use cases appear where departments meet.
With your management: clarify the goals and tailor the workshop to your company.
About half a day on site with your department heads — agenda as above.
Presentation, the evaluated AI readiness survey, prioritised use cases and a proposal for how to proceed.
Go through the results together and agree the next steps.
A picture of your situation
An anonymous self-assessment from every department, evaluated and put in context.
Prioritised recommendations
Tailored to your company and sorted by benefit and effort.
A shared understanding
Every department head leaves with the same realistic picture of AI.
A concrete efficiency roadmap
A proposal for which AI use cases to start with — and where they save money.
Your contacts
Wolfe Diener & Maxi Wegele
Founder & CEO · Product Owner & Web Designer
Prefer to talk first?
A short kick-off call is enough to agree where the workshop should focus for you — free and without obligation.
Request the workshopToken prices keep falling, yet AI bills keep rising. Six patterns explain why — and each has countermeasures. Four of the six are architecture and process questions, which is why AI cost control is management work, not a procurement detail.
up to ~1,000×
AI models are stateless — agents resend the growing history in full with every step, so agentic tasks can cost up to ~1,000× more tokens than a simple chat.
Countermeasures: Prompt caching, context hygiene, prefer single-turn where possible.
~60 %
The first answer is not the expensive part — checking, repairing and re-verifying it afterwards is. Around 60 % of an agent task’s cost sits in refinement.
Countermeasures: Invest up front, verify with cheap models or fixed rules, measure the rework rate.
up to 30×
The same job can cost wildly different amounts depending on the path the agent chooses — same task, up to 30× cost difference per run.
Countermeasures: Set stop rules, dose autonomy, monitor the outliers — budget the distribution, not the average.
overhead
Defaulting to the strongest model for everything is pure overhead on simple tasks — expensive reasoning belongs where decisions are made, not in routine work.
Countermeasures: Fix the model per use case, cap the thinking budget, frontier models only where they change the outcome.
paid repeatedly
How work is split across agents, tools and middleware changes the bill dramatically — the same tokens are often processed and priced several times on their way to the model.
Countermeasures: Cost the full run, keep handovers lean, use few middleware layers.
−30–40 %
How information is phrased and formatted determines the token count — identical content, up to 30–40 % savings through tight, structured inputs and outputs.
Countermeasures: Write tightly, use structured formats, make costs visible per run.
Cost drivers after McKinsey & Company · countermeasures: NoviCogi · Read the study
Download the infographic (PDF)In 30 minutes we sort out together which use cases have potential — and which do not. Free, without obligation, GDPR-compliant.
