CityPulse Studio

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  Interactive analysis state where users provide urban context and trigger Gemini-powered intelligence generation.

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  Upload workflow for structured and unstructured city data, including outage CSVs and planning documents.

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  Detailed insights panel where Gemini 3 analyzes CSV and PDF inputs to surface risks, trends, and recommendations.

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  CityPulse Studio dashboard showing AI-generated urban intelligence summaries from uploaded datasets.

Inspiration

City data is often scattered across CSV files and long planning PDFs, making it difficult for planners, researchers, and citizens to extract meaningful insights quickly. I wanted to build a tool that could bridge this gap and turn raw urban data into clear, actionable intelligence.

What the Project Does

CityPulse Studio is an AI-powered urban intelligence platform that combines structured CSV datasets with unstructured PDF planning documents to generate summaries, insights, and recommendations for city decision-making.

Gemini Integration

CityPulse Studio is built around Gemini 3 and uses it as the core reasoning engine. Gemini processes structured CSV data such as outage records and service metrics alongside unstructured PDF planning documents to understand urban context holistically. By leveraging Gemini’s multimodal and long-context capabilities, the system can summarize trends, detect patterns, and generate actionable recommendations in natural language.

Users can also add voice or text-based urban context, which Gemini interprets to refine its analysis. This allows the platform to adapt insights based on local challenges, citizen feedback, or specific goals. Gemini’s reasoning and summarization capabilities enable CityPulse Studio to transform complex city data into clear intelligence that is easy to understand and act upon.

How I Built It

The application was built using Google AI Studio with Gemini 3, combining a simple web interface with backend logic to process uploaded CSV and PDF files. The focus was on fast prototyping, usability, and real-world relevance.

Challenges & Learnings

One key challenge was aligning insights across structured and unstructured data sources. This project demonstrated how Gemini 3 can effectively reason across multiple data formats and highlighted the power of AI-assisted urban analytics.

Built With

• css
• gemini-3-api
• google-ai-studio
• html
• javascript
• node.js
• webpack