Webinar Invitation: Making Product Knowledge AI-Ready: Lessons from KAESER

Webinar Graphwise x PANTOPIX x KAESER Kompressoren

18. June 2026

The webinar hosted by Graphwise, KAESER Compressors, and PANTOPIX will take place on July 21, 2026, at 4:00 p.m. The focus will be on how product knowledge can be made AI-ready using semantic structures and knowledge graphs.

AI applications need more than just data: they require semantic structures that make information from different systems understandable and link it together.

Using the KAESER Knowledge Hub as an example, the speakers demonstrate how product information from SAP and other enterprise systems was transferred into a unified knowledge graph. Today, this graph serves as the semantic foundation for applications and processes throughout the company.

A particular focus is on the shared knowledge model, which not only supports the graph architecture but also enables data integration, transformation, and automated publishing processes. With Graphwise GraphDB serving as the operational foundation, a data-driven approach emerges that connects semantic models, enterprise data, and user-centric applications.

To conclude, the webinar provides an overview of the KAESER Product Navigator—a concrete, real-world example of how a knowledge graph can be transformed into a scalable, AI-enabled application.

What you’ll learn in the webinar:

  • How a shared knowledge model supports graph architecture and data workflows
  • How SAP and other enterprise data sources are semantically linked
  • How Knowledge Graphs Enable Transformation and Publishing Processes
  • How a user-oriented graph application is created from a semantic model

Our speakers

photo Karsten Schrempp PANTOPIX

Karsten Schrempp

Managing Director | PANTOPIX
Hella Tischer | Systems Manager Knowledge & Cross Applications, KAESER Kompressoren

Hella Tischer

Systems Manager Knowledge & Cross Applications | KAESER Kompressoren
Helmut Nagy | VP Sales Enablement, Graphwise

Helmut Nagy

VP Sales Enablement | Graphwise

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