Normalized claim
Productivity: 65% increase
IT administration productivity increased by 65%.
City of Chattanooga builds AI-powered smart city capabilities with Vertex AI, BigQuery, and AI agents
Use case typeGrowth analyticsUpdated Jun 13, 2026
The City of Chattanooga is using Google Cloud to modernize internal productivity and build smart city capabilities across public safety, city operations, and resident engagement. The city centralized data in BigQuery, trained a Vertex AI model on municipal code, and began rolling out Google Workspace with Gemini and NotebookLM to employees.
- Organization
- City of Chattanooga
- Industry
- Public Sector
- Location
- United States
- Published
- May 2026
Reported outcomes
+65%
productivityProductivity & throughput
−75%time
Strategic outcomes
Speed & agilityReduced regulatory research to secondsScale & capacityBuilt resident-feedback automation foundationNew product / capabilityCreated natural-language code Q&A capabilityBetter decisions & insightEstablished trusted analytics for road safety
Catalog median for productivity & throughput deployments: +40% across 108 reported metrics. Compare benchmarks →
Why do we believe this?Outcome claims, sources, and evidence checks
Normalized claim
Time: 75% decrease
Correspondence drafting time dropped by more than 75%.
Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks
- Customer
- City of Chattanooga
- Provider
- GCP
- Maturity
- Scaled Production
- Linked source
- Google Cloud Customer Story
Staff needed faster answers from dense municipal code documents and a way to respond to resident feedback at scale
Customer identity supportedSource describes one deploymentMaturity supported
Primary read
Use case focus
Showing 3 of 4
- 1Smart city analytics
- 2Document question answering
- 3Employee productivity
- City IT staff were spending most of their time on maintenance of aging systems instead of strategic work.
- Operational data was siloed across many disconnected systems, limiting cross-functional analysis.
- Staff needed faster answers from dense municipal code documents and a way to respond to resident feedback at scale.
- The city adopted Google Workspace as the collaboration foundation and Google Workspace with Gemini to improve productivity.
- It created a single source of truth in BigQuery by centralizing datasets from internal systems and public data sources.
- The city uses Looker to build trusted analytics and mapping views for road safety and crash hotspot analysis.
- Using Vertex AI, Chattanooga trained a model on municipal code to answer natural-language questions with cited responses.
- The city is planning to deploy Vertex AI Agent Builder to ingest and analyze feedback from 311, email, and texts.
- IT administration productivity increased by 65%.
- Correspondence drafting time dropped by more than 75%.
- Regulatory research time was reduced from hours to seconds.
- The city established a reusable data foundation for crash analytics and resident-feedback automation.
Architecture
Google Cloud architecture centered on BigQuery as the system of record, Looker for semantic analytics and dashboards, Vertex AI for municipal-code question answering, Google Workspace with Gemini and NotebookLM for productivity, and planned Vertex AI Agent Builder workflows for resident feedback analysis.
Sources & evidence1
Evidence: Medium65/100Evidence strength
- Customer explicitly identified
- Deployment status explicitly supported
- Primary source available
- Quantified outcome available
- Technical implementation details available
Type: Customer StoryPublished: May 13, 2026Publisher: Google Cloud Customer StoryEvidence: PrimaryConfidence: High
AI-generated summary. Verify important details with the linked sources before relying on this case.
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