IPA

A central focus of the AI Innovation Center is direct cooperation with industrial companies: Feasibility studies (Quick Checks) and projects to develop initial prototypes (Exploring Projects) of AI applications. In addition, consortium projects and innovation networks are aimed at cross-company exchange.

Following the successful completion of the pilot phase, the KIRR Real project was integrated into and expanded within the AI Advancement Center. This expands the cooperation offerings to include regulatory analyses (Legal Quick Checks). The results of the Legal Quick Checks from the pilot phase are available on the KIRR Real Homepage documented. Legal Quick Checks from the AI Advancement Center will be published on this page.

Development and innovation

Logistics and merchandise management

Marketing and communication

Production and quality management

Administration and organization

Legal Analysis

AI Beyond the Prototype

Requirements for the robust continuous operation of AI in the context of production Artificial intelligence (AI) offers significant advantages in industrial applications, including the reduction of resource consumption, better adjusted machine parameters or a more...

EnayaKI (Enayati Calculation Interface)

In the project, an AI was developed that automatically analyzes customer inquiries. The information is directly transferred to quote sheets, which significantly shortens quote processing times and creates a competitive advantage through faster responsiveness.

AI agent system for scheduling

An AI-based agent system optimizes therapy appointment scheduling in a rehabilitation clinic by considering complex constraints such as sequences, resources, and even distribution of applications. Particularly innovative is the ability to add new requirements via natural language as modular rules, which keeps the scheduling flexibly and efficiently adaptable.

AI Competence in Customer Support

In the project, potentials for the use of AI in customer service were identified and prioritized to sustainably increase service efficiency through forward-looking planning, intelligent automation, and improved information utilization. Based on a structured analysis, concrete implementation concepts are being developed for the three most promising use cases: social workforce planning, predictive maintenance, and an AI-powered assistance system.

ML-supported prediction of screw straightening quality

An ML model predicts the change in deflection of bolts during the straightening process using measurement data and process parameters, enabling automation and quality assurance. Classification, in particular, is highly successful, predicting with high accuracy whether the bolts will be within tolerance after pressing.

Robot Safety in Industry 4.0

Trends, Challenges and Future Opportunities Robots are leaving their cages. With the acceleration of Industry 4.0, manufacturers are moving from fenced-in automation to collaborative and even humanoid systems - creating new opportunities for...

Reliable AI

Using AI in safety-critical industrial applications A great deal of research has been carried out in the field of artificial intelligence (AI) in recent years. Promising applications have great potential in production, medicine and autonomous...