, Helium [3rd Floor]
The City of Munich is modernizing its communication: With the transition to the Zammad ticketing system, there is a unique opportunity to not only manage citizen inquiries but to proactively process them using Artificial Intelligence. The Zammad-AI project utilizes a two-stage process consisting of intelligent classification and RAG-based (Retrieval-Augmented Generation) response drafting to significantly reduce the workload of administrative staff.
In this talk, we demonstrate how we integrated Zammad-AI via an internal Kafka message bus to process tickets in real-time. We explore the technical workflow—from thematic context analysis to the generation of valid response drafts based on a department-specific knowledge base.
3. Session Outline (30 Minutes)
I. Context & The Pre-Study | 5 min
- The Shift: Transitioning from legacy email communication to Zammad within Munich's city administration.
- Proving the Case: Utilizing LLMs to analyze historical ticket data to calculate automation potential and project significant time savings before development began.
II. Architecture: Integration & Pipeline | 6 min
- Event-Driven Design: Connecting to Zammad via the city-internal Kafka message bus.
- Real-time Processing: How new tickets are captured and routed to the AI component seamlessly.
III. The Two-Stage Process | 12 min
- Step 1: Classification & Extraction: Analyzing thematic context through rule-based logic and LLM-powered information extraction.
- Step 2: Response Generation: A RAG (Retrieval-Augmented Generation) approach leveraging a knowledge base maintained by subject matter experts.
- Human-in-the-Loop: Integrating response drafts into the agent UI for review vs. automated "dark processing" for high-confidence categories.
IV. Scaling & Lessons Learned | 4 min
- Multi-Tenant Capability: Designing for configurability and deployment across various city departments.
- Key Benefits: Efficiency gains, response consistency, and establishing a "Single Voice of the City."
V. Q&A | 3 min
- Open discussion on technical tooling, model selection, and legal/privacy frameworks.
4. Key Takeaways for Attendees
- Validating Automation: Techniques for using LLMs to audit historical data and justify development through projected time savings.
- Practical AI Integration: How to integrate AI services into existing enterprise infrastructures like Zammad and Kafka.
- Modular Workflow: The importance of separating classification from generation for higher system reliability.
- Operational Insights: Lessons from scaling AI solutions across diverse governmental branches.
Leon Lukas has been the team lead of the AI Competence Center for two years and has played a key role in the development and implementation of AI solutions within the city administration. While he initially trained models and built systems himself, he is now responsible for the architecture and projects at it@m, the city’s IT service provider. For more information on AI in the City of Munich, visit: ki.muenchen.de.