Know where AI actually moves the needle.
We check data, systems, and organization before you invest in pilot projects that lead nowhere.
The situation
Many companies launch AI initiatives without knowing whether their data situation, system landscape, and organization can support them. The result is pilot projects that work technically but never go into production, because data is missing, interfaces don't exist, or nobody takes responsibility for running them.
We assess before the investment: which processes deliver enough clean data for an agent or a model? Which systems can be connected, and which are a dead end? Who in the company can operate and be accountable for an AI system?
The end result isn't a general maturity score, but a prioritized list: which use cases are worth pursuing, in what order, and what's missing in terms of data or systems for each.
Deliverables
Data assessment
A review of which processes deliver structured, complete, and current data for AI applications.
System map
An overview of the relevant systems, interfaces, and integration effort.
Organizational view
An assessment of who in the company can operate, monitor, and be accountable for use cases.
Prioritized use cases
Concrete use cases, rated by effort and impact on results or cycle time.
Gap list
A concrete list of what's missing in data, systems, or processes before the first use case can start.
Decision document
A compact document for leadership or the investment committee, not an extensive study.
Our approach
- 01
Clarify the target picture
We clarify with leadership which business outcome the assessment should inform.
- 02
Review systems and data
We check core systems, data models, and interfaces with the responsible teams.
- 03
Assess the organization
We talk to business units and IT to assess operational capability and capacity.
- 04
Evaluate and prioritize use cases
We rate potential use cases by data situation, effort, and impact.
- 05
Hand over report and recommendation
We hand over the prioritized list and the concrete next steps.
Typical starting points
| Starting point | Result |
|---|---|
| Leadership wants to justify an AI investment | A solid basis instead of a guess for the next investment decision |
| Several pilots without moving into production | Clarity on the cause: data, systems, or organization |
| A PE fund reviews a portfolio company before rollout | A comparable assessment across several portfolio companies |
Result: A solid basis instead of a guess for the next investment decision
Result: Clarity on the cause: data, systems, or organization
Result: A comparable assessment across several portfolio companies
Outside our scope
We don't deliver a generic maturity matrix with a score, and no technology recommendation unrelated to a concrete use case. An assessment without a follow-on implementation is possible, but we say openly when a use case isn't mature enough to start.
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 look at your data and system situation and tell you honestly where an AI investment pays off, and where it doesn't.