Artificial Intelligence –
applied, explained, experienced
We make AI visible: interactive dashboards, generative design and hands-on education for companies that don’t just want to use AI, but truly understand it.
What we deliver
Four focus areas with direct links to real-world examples.
Dashboards & Statistical Forecasting
Interactive real-time dashboards with predictive analytics: from KPI overviews to ML-driven forecasts. Raw data becomes reliable decision-making input.
Data Visualization
Complex datasets made visual – from scientific plots to interactive web visualizations. Also specialized: biological structure data, AlphaFold outputs & more.
Branding & Design with AI
Adobe Firefly for consistent brand assets: icons, illustrations, social media content. Generative AI as a creative tool always focused on your brand identity.
AI Chatbots & Dynamic Content
From simple FAQ bots to NLP-powered assistants that autonomously qualify and route customer inquiries. Knut, the polar bear in the bottom right – is a live example: privacy-compliant, European, EU AI Act-ready.
Dynamic content means: your website speaks to every visitor individually through personalized content, language, or layout based on location, time, or behavior.
Learn & Apply AI Tools
Practical introductions – no dry theory courses, but hands-on: prompting strategies, generative image tools (Firefly), scientific AI (AlphaFold, protein structures), industry-specific tools.
AI You Can Touch
Four interactive demos showcasing real methods – with direct business relevance. All data in the demos is synthetic unless otherwise noted.
Customer Segmentation with K-Means
Click on the canvas to place customer data points (e.g. purchase frequency vs. basket value). Press “Segment” – the algorithm automatically groups them into k clusters. In practice: the basis for targeted marketing campaigns.
Which Customer Will Churn Next?
Adjust the sliders and watch how customer clusters shift between “stays” and “at risk.” In practice: proactive retention measures before cancellation.
✦ Synthetic data for method illustration
Credit Decision with SHAP Explanation
Set the customer data – the AI decides and explains which factors were weighted and how strongly. In practice: transparent decisions per GDPR Art. 22 & EU AI Act.
✦ Synthetic data · Illustrative model · Not financial advice
Revenue Forecast: What-If Scenarios
Set market parameters and see how an ML model forecasts annual revenue in 12-month steps – with confidence interval. In practice: the basis for budget planning and investment decisions.
✦ Synthetic model · Not financial advice
Experience Dynamic Content Live
Choose a scenario: Content, language, and presentation adapt automatically. Your selection is captured in real time and displayed in the chart.
Dynamic Content
This section demonstrates how content can change based on context – location, time of day, language, and more.
Ready to Start a Project Together?
Whether it’s a dashboard, generative design, or an AI workshop – we’re happy to advise you, no strings attached.
What Are SHAP Values?
SHAP (SHapley Additive exPlanations) explains how much each feature contributed to an AI model’s decision. A high positive SHAP value means: this feature strongly pushed the decision toward “approval.” Negative values have the opposite effect.
This method is part of Explainable AI (XAI) – a key concept for transparent, fair, and regulation-compliant AI systems.