AI Transformation that shows up in EBITDA.
We find the three to five AI levers with the greatest impact on your results and build them, from analysis to a running system.
The situation
Most companies have experimented with AI by now. A chatbot pilot here, a copilot there. What's missing is the move from demo to routine operation, and the question of which of the many possible applications actually moves margin, cycle time, or error rate. That's exactly the problem: a lack of prioritization driven by outcome instead of technology.
So we start from the number, not the model. First, we identify where in your processes time, errors, or costs arise that can be reduced with AI agents or workflow automation. Only then comes the technical decision: language model, automation platform, custom build, or SaaS.
What changes for you: scattered pilot projects become a roadmap with clear business cases, and the roadmap becomes systems running in daily operation. We stay until your team can operate and extend the systems on its own.
Deliverables
AI readiness analysis
A structured view of processes, data, and systems: where the levers with real impact lie, what's technically feasible, what's organizationally sustainable.
Prioritized roadmap with business cases
Use cases sorted by effort and impact, with an estimate of the effect on margin, cycle time, or cost for each lever.
Prototypes with real data
The most promising use cases are built as working prototypes and tested with real data and real users before you invest further.
Production-ready implementation
AI agents, workflows, and integrations running in daily operation, with monitoring, error handling, and a clear owner.
Team enablement
Your team understands the systems, can maintain, adjust, and extend them, instead of staying permanently dependent on us.
Governance foundation
Documented decisions on data protection, model choice, and responsibilities, so the solution also holds up to scrutiny by investors or boards.
Our approach
- 01
Analysis
We review processes, data, and systems and identify the levers with the greatest impact, together with the business units, not just IT.
- 02
Prioritization
We rank the use cases by effort and impact and align the roadmap with the leadership team. What doesn't pay off in the first steps comes later or not at all.
- 03
Build
We develop the prioritized use cases in small, testable steps and validate each step against real data before the next one begins.
- 04
Bring into production
We move the solution into routine operation: monitoring, responsibilities, error handling, and an operating plan are part of it.
- 05
Scale
We extend the solution to further processes and enable your team to evaluate and implement future use cases on its own.
Typical clients
| Role | Concern |
|---|---|
| CEO / leadership of an owner-led company | AI should create margin or capacity, not stay a pilot project. |
| COO / Operations | Reduce cycle times and error rates in customer service, logistics, or back office. |
| PE operating partner | Make AI value creation in a portfolio company plannable, with impact on the value creation plan. |
| CTO / head of IT | An external assessment of which AI investment pays off before building it in-house. |
Concern: AI should create margin or capacity, not stay a pilot project.
Concern: Reduce cycle times and error rates in customer service, logistics, or back office.
Concern: Make AI value creation in a portfolio company plannable, with impact on the value creation plan.
Concern: An external assessment of which AI investment pays off before building it in-house.
Outside our scope
We don't deliver a strategy presentation without an implementation path, and no pilot project without a route into production. We don't take on general organizational consulting, change-management programs, or HR topics around AI adoption. Specialized partners exist for that. And we don't build on every available technology: if an existing SaaS tool solves the requirement more cheaply and faster, we recommend it.
Frequently asked questions
| Ausgangslage | Umsetzung | Ergebnis | |
|---|---|---|---|
| EdTech company, PE-financed | PE-financed EdTech company, technical picture unclear before the investment. | Tech DD, greenfield architecture, AI agents in production, AI-assisted engineering organization. | The company runs AI in day-to-day operations, not just in pilot projects. |
| EdTech company, content production | Content production ran step by step by hand and depended on a few people. | n8n pipeline with LLM steps for drafting, structuring, and formatting, with human sign-off. | The content team works on quality, not on mechanical intermediate steps. |
| EdTech company, customer service | Customer service answered recurring questions manually from scattered knowledge. | AI agent with access to the knowledge base, with clear escalation to humans. | Recurring inquiries run productively through the agent, complex cases stay with the team. |
Ausgangslage
PE-financed EdTech company, technical picture unclear before the investment.
Umsetzung
Tech DD, greenfield architecture, AI agents in production, AI-assisted engineering organization.
Ergebnis
The company runs AI in day-to-day operations, not just in pilot projects.
EdTech company, content production
Ausgangslage
Content production ran step by step by hand and depended on a few people.
Umsetzung
n8n pipeline with LLM steps for drafting, structuring, and formatting, with human sign-off.
Ergebnis
The content team works on quality, not on mechanical intermediate steps.
EdTech company, customer service
Ausgangslage
Customer service answered recurring questions manually from scattered knowledge.
Umsetzung
AI agent with access to the knowledge base, with clear escalation to humans.
Ergebnis
Recurring inquiries run productively through the agent, complex cases stay with the team.
Initial call: 30 minutes, concrete.
We talk through your processes and name the one or two levers with the greatest impact on results during the call.