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Powering Government with Community Voices: How ZenCity Built an AI That Listens

Powering Government with Community Voices: How ZenCity Built an AI That Listens

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How do you use AI to help city leaders truly hear their residents?

In this episode, Teresa Torres talks with Noa Reikhav (Head of Product), Andrew Therriault (VP of Data Science), and Shota Papiashvili (SVP of R&D) from Zencity, a company that powers government decision-making with community voices.

They share how Zencity brings together survey data, 311 calls, social media, and local news into a unified platform that helps cities understand what people care about—and act on it. You’ll hear how the team built their AI assistant and workflow engine by being thoughtful about their data layers, how they combined deterministic systems with LLM-driven synthesis, and how they keep accuracy and trust at the core of every AI decision.

It’s a fascinating look at how modern AI infrastructure can turn noisy, messy civic data into clear, actionable insight.

Show Notes

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

Full Transcript

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