Projekte und Referenzen
Mit mehr als 50 aktiven Kunden haben wir diverse Anwendungsfälle umgesetzt. Hier stellen wir eine Auswahl der Projekte vor.
Mar 18, 2024
Startup meets Scaleup
A strategic partnership between a small AI-startup and a leading digital health company helped both organisations grow together – with Kineo helping Temedica revolutionise the digital health domain
Mar 18, 2024
Predictive Quality in decorative wood board production
We all have it at home: Furniture, kitchens or laminates. If you are looking for a high class finish in your place to live there is no way around the classic wooden optic. Using complete cutouts bears many challenges, such as weight, required craftsmanship for precise processing and even finding the exact piece of wood you need to complete the look. That’s why many wood appliances and surfaces are often made out of wood-based materials that have been shredded and pressed together. The big question is: Can AI improve the quality of these products? In this case study we want to talk about the challenges we overcame when applying machine learning to actual wood processing production lines.
Mar 18, 2024
Opening the doors to AI-driven sales forecasting
How Kineo AI powered solutions helped Marantec, a high class opener technology company, to improve their sales forecast.
Mar 18, 2024
From 0 to AI in a day: How to find your AI use cases
Everyone has already heard about the buzzwords Artificial Intelligence (AI) and Machine Learning (ML). But knowing when and where to apply them is the hard part. With the AI Workshop, Kineo.ai is offering companies a way to get a fundamental understanding of applying AI and helps them to elicit the use cases that bring the biggest business value. Let’s take a closer look at how the AI Workshop has helped one of the leading ergonomic goods handling companies in Germany.
Mar 18, 2024
When the Netflix approach doesn’t work: Personalized Travel Recommendations
The customer needed to personalize hotel and travel offers to their customers across their entire portfolio. Crucially, very little information about the customers was available. In particular no Login was being captured, so typical recommender approaches didn’t quite work.
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