From Ticket to Draft: How Munich Automates Citizen Inquiries with AI

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.