Your AI Pilot Project.
Technically proven.
Individually implemented.

An AI pilot is an implementation format that proves whether AI can deliver value in your organisation. It makes visible what concrete benefit AI creates in your specific context and which technical approaches are viable. Ideas become verifiable technical results and presentable facts.

In the AI pilot, we pilot the critical technical components of an AI initiative: data quality and data structure, technology approaches, decision logic, models, architecture principles, and interaction. Different technical paths are implemented and compared. This creates clarity about which approach works, why it works, and where its limitations lie.

The AI pilot is agile and rigorously implementation-driven. Research and development are part of the process to practically resolve open questions about technology, data, and feasibility.

The result is a prototype as version 0.1 of a new AI system. It is usable, presentable, and explainable. It translates abstract expectations into visible results. Insights come from hands-on use, not from slides. The outcome provides a solid foundation for informed decisions about next steps.

An AI pilot project is ideal for companies that want to leverage AI but refuse to base decisions on assumptions.

It is designed for mid-sized enterprises and corporations with established processes and system landscapes where off-the-shelf software, generic SaaS solutions, or basic prompting simply cannot meet their requirements. The relevant AI use cases are too specific, too deeply integrated, or too business-critical.

A typical situation: the need for AI is clearly recognised, but uncertainty remains. Is the data suitable? Which technology actually works? Can the AI use case be implemented in a way that is both technically and economically viable?

This is exactly where the AI pilot project comes in. It enables a low-risk entry with manageable effort and delivers reliable insights before larger investments are committed. Instead of theoretical assessments, you get verifiable results based on real data.

The AI pilot project is especially suited for companies that:

• Want to technically validate AI use cases before scaling investment
• Need clarity on feasibility, value, and limitations
• Want to compare technology approaches in practice and make informed choices
• Prefer to decide on next steps based on solid evidence

An AI pilot is deliberately short and focused. It typically takes a few weeks, depending on the use case and data situation.

This timeframe is sufficient to analyse real data, implement different AI approaches, and build a working prototype. The result is clarity: what works, what does not, and how to proceed.

To kick off an AI pilot, we need a clearly described use case, access to relevant data, and domain experts as points of contact.

It starts with a jointly defined use case and a concrete question: What task should AI take on, or what decision should it support? What do we want to find out in the prototype? This scoping takes place in the kick-off workshop, where we discuss objectives, data availability, and possible technical approaches.

On the client side, we need subject matter experts who can explain the use case, contribute domain knowledge, and answer questions about the data. IT resources are not required. The AI pilot is implemented independently and does not require additional project or IT structures.

Typically, the resource commitment on the client side is manageable. Beyond the designated points of contact and access to relevant data, no extensive internal capacities are needed.

Collaboration in the AI pilot begins with a joint kick-off workshop where the question, objectives, and approach are defined. We then work in three phases: first, the pilot is assessed from a domain, technical, and data perspective; then different AI approaches are implemented and tested; and finally, results are evaluated and contextualised.

Throughout the entire project, regular status meetings ensure that progress, interim results, and insights are continuously aligned. Results are made visible early, reviewed jointly, and iteratively refined.

Yes, it can. That is exactly what the AI pilot project is designed to find out.

Not every AI use case can be implemented effectively. The AI pilot project is deliberately conceived as an exploratory implementation format. It tests with real data and real code whether and how AI can take on a specific task. In some cases, it turns out that AI does not deliver the desired value given the available data, constraints, or objectives.

This is not a failure but a valid outcome of the AI use case assessment: clarity instead of assumptions. It prevents misguided investments and provides valuable input for a better, well-founded AI strategy.

Important to note: we assess whether an AI use case is fundamentally realistic before the project even starts. Ideas that, based on our experience, are not technically or practically viable, we decline. Within the project itself, we then determine how well AI can solve the task – or where its limitations lie.

That is your decision. The AI pilot project delivers a solid decision basis for the next phase of the project. From there, several meaningful paths emerge:

• Continue into production
  We evolve the AI prototype into a production-ready AI implementation within a 100-day MVP, or take over development, operations, and scaling through our AI Tech Team.
• Internal implementation
  You use the insights gained, the prototype, and the technical clarity to continue the project in-house.
• Strategic realignment
  If the pilot reveals that AI cannot yet effectively handle the task, we use the findings to refine your AI strategy and develop alternative approaches.

In every case, the project does not end with open questions but with clarity. And that is what matters.

Our KPIs are not for marketing but for realistically assessing result quality. They transparently show how well the AI system actually performs its task, traceable, explainable, and comparable over time.

Which KPIs are meaningful depends on the specific use case. That is why we recommend the appropriate metrics, explain the rationale behind their selection, and clarify what they mean in business terms. Examples include:

• Prediction accuracy: How close the AI gets to the expected or known result.
  Examples: R², MAE (Mean Absolute Error), RMSE, MAPE, Accuracy
• Error types and error distribution: What types of errors occur and which of them are critical or tolerable in the business process.
  Examples: Confusion Matrix, False Positives / False Negatives, Precision, Recall, F1-Score
• Model drift: Detection of changes in data or patterns that cause the model to gradually lose predictive power.
  Examples: Data Drift, Prediction Drift, Population Stability Index (PSI), Feature Shift
• Response quality for agents: Assessment of factual correctness, source grounding, and goal achievement of responses.
  Examples: Mean Reciprocal Rank (MRR), Retrieval Precision / Recall (R-P / R-R), Context Coverage (CC), Groundedness Score (GS), Hallucination Rate (HR), Task Success Rate (TSR).
• Cost savings: What impact the AI results have on costs, turnaround times, risks, or revenue.
  Examples: Time saved per process, reduction of manual reviews, avoided error costs, automation rate‍
• Cost-performance ratio: The relationship between computational effort, runtime, infrastructure costs, and achieved model performance.
  Examples: Inference cost per prediction, latency, performance per euro of cloud costs

A proof of concept (PoC) demonstrates that a technology works in principle. Our AI pilot project goes further: it delivers not only proof of feasibility but a usable AI prototype, measurable results, and a technical decision basis for AI implementation.

The AI pilot includes use case assessment and prioritisation, comparison of different AI technologies with real data, building a working prototype, and documented evaluation. The result is not a slide deck but a system you can test yourself.

For companies looking to adopt AI strategically, the AI pilot is the more robust starting point: it combines the validation of a PoC with the implementation depth of a first real project.

Because we deliver AI end-to-end – from AI pilot project to production. As an AI service provider, we take responsibility for the outcome, not just for individual project phases. What sets us apart as an AI service provider:

• End-to-end delivery: Strategy, AI use case refinement, architecture, custom AI models, software engineering, cloud infrastructure, deployment, operations, and continuous improvement. No handoffs, no gaps.
• Custom AI model training: We build tailored AI models with real data science – no prompting wrappers, no drag-and-drop AI, no generic off-the-shelf solutions.
• Technical depth with business focus: Project decisions are driven by value, risk, and ROI – not by trends or tool recommendations.
• Ownership through go-live and beyond: The 100-day MVP ends with production deployment. On request, we continue with operations, support, and scaling.
• Experience from real AI projects: AI development since 2017. Hundreds of completed AI implementations for enterprises with high demands on integration, security, and scalability.‍
• Compliance & security: We develop, operate, and maintain AI systems in Germany under ISO 27001. Encryption, anonymisation, clean architectures, and traceable documentation are part of our DNA.