The project focuses on implementing an LLM-based solution that uses historical ticket data and freely available technical documentation to analyze service tickets. The system provides automatic recommendations, enabling companies to determine whether an issue can be resolved remotely or if a field service visit with specific spare parts is required.
This approach is designed to improve remote problem resolution and increase the first-time fix rate, reducing unnecessary site visits and saving valuable resources. The pilot project is already in progress, with more updates to follow as implementation advances.
Key Objectives of the LLM-Supported Ticket Analysis
-Optimized Remote Resolution: By harnessing advanced analytics, the system identifies when a problem can be fixed remotely, reducing the need for in-person interventions and lowering carbon emissions linked to travel.
-Accurate Spare Parts Recommendations: The solution offers precise recommendations on the necessary spare parts for field service tasks, preventing over-purchasing, lowering shipping costs, and minimizing waste.
-Data-Driven Decision Making: The system leverages historical ticket data and available technical information to make well-founded, data-backed decisions.
About Green-AI Hub
Green-AI Hub supports enterprises across Germany in implementing AI projects that save resources. As part of pilot projects, AI experts collaborate with selected companies to develop sustainable AI solutions over six months. The Green-AI Hub aims to complete up to 20 pilot applications by the end of 2025. Companies that join the program receive on-site support, access AI expertise, and design modern AI applications that conserve resources.