Guests
- Noa Reikhav, Head of Product, Zencity
- Andrew Therriault, VP of Data Science, Zencity
- Shota Papiashvili, SVP of R&D, Zencity
In this episode
- How Zencity helps local governments reach, understand, and act on community voices
- Turning thousands of survey responses, social posts, 311 calls, and news items into usable insight
- Building a data model with multiple layers—raw data → elements → highlights → insights → briefs
- Why context is everything when building AI for civic use
- How the team designed their AI assistant using MCP servers to safely negotiate data access
- Balancing agentic flexibility with deterministic trust
- Evaluating accuracy when latency matters: how they think about evals, citations, and model-as-judge systems
- Using workflows like annual budgeting or crisis communication to deliver AI-generated briefs to the right people at the right time
- Why government workflows are the ultimate “jobs to be done” framework
Takeaways
- Data architecture defines what AI can do.
- Guardrails and transparency matter more than flashy outputs.
- Agentic systems become powerful when grounded in real, multi-tenant data.
- AI in the public sector can make democracy more responsive—if built responsibly.
Chapters:
00:00 Introduction to the Team
00:16 What is ZenCity?
01:26 AI in ZenCity's Platform
06:00 Survey Methodologies and Use Cases
09:01 Community Voices and Social Listening
14:36 Workflows and AI Integration
22:15 Annual Budget Planning Workflow
32:44 Data Layers and Sentiment Analysis
33:53 Post Interaction Surveys and Resident Engagement
34:20 Data Enrichment and Sentiment Analysis
35:14 Topic Modeling and Semantic Search
36:50 AI Content Summarization and User-Driven AI Assistant
38:53 Highlights, Insights, and the Gold Layer
41:19 Challenges and Solutions in AI Data Processing
46:47 AI Assistant and Guardrails
01:05:27 Future Developments and Orchestration Layer
01:06:44 Conclusion and Final Thoughts
