Inspiration

Reporting sick or dead wildlife should be simple, fast and accessible. In reality, many reporting processes are fragmented, require multiple steps, and often result in incomplete information. Spot & Report was created to make environmental reporting easier for the public while improving the quality of information received by agencies.

What it does

Spot & Report guides users through reporting an environmental incident in under a minute.

Users can:

Capture a photo
Record their location
Describe what they observed
Receive an AI-generated report summary
Review the report before submission

The goal is to help users produce clearer, more consistent reports while reducing manual effort.

How we built it

The application was built using Next.js, React, TypeScript and Tailwind CSS.

OpenAI GPT-5.6 powers:

AI-generated report summaries
AI-assisted image analysis
Structured environmental observations

Reports are stored using Supabase with private image storage, while the application is deployed on Vercel.

Development was accelerated using OpenAI Codex, which assisted with implementation, testing, debugging and documentation throughout the project.

Challenges we ran into

The biggest challenge was integrating AI into a production-ready reporting workflow within a limited Build Week timeframe.

Considerable time was spent refining the submission flow, validating uploaded images, configuring Supabase storage, handling production deployments and ensuring AI outputs remained useful and structured.

Accomplishments that we're proud of

Built a complete end-to-end reporting workflow
Integrated GPT-5.6 for summaries and image analysis
Implemented secure cloud storage with Supabase
Deployed a production-ready web application
Created a clean, mobile-friendly reporting experience

What we learned

This project demonstrated how AI can improve public reporting by reducing friction while producing more structured, useful information.

It also reinforced the importance of production testing, deployment workflows and iterative debugging when integrating AI into real-world applications.