TraceGuard AI

Inspiration

Modern software applications generate large volumes of logs, making it difficult for developers to quickly identify the root causes of failures. During incidents, manually searching through repeated errors and unrelated log entries can waste valuable debugging time. We built TraceGuard AI to make application log analysis more structured and help developers identify suspicious patterns and investigate incidents more efficiently.

What It Does

TraceGuard AI is an AI-powered application log analysis platform designed to help developers investigate application errors and operational anomalies. It aims to turn raw log data into clearer, actionable insights by highlighting unusual patterns, recurring errors, and potential relationships between failures.

How We Built It

We built TraceGuard AI as a full-stack web application with a React and TypeScript frontend and a FastAPI backend. We deployed the frontend and backend separately, using Vercel for the frontend and Render for the backend. Our focus was to create a working, publicly accessible application rather than just a concept or prototype on a local machine.

Challenges We Faced

One of our main challenges was deploying the frontend and backend as separate services while configuring environment variables, API connectivity, and database settings correctly. We also worked through deployment configuration issues to get the application running in a live environment.

What We Learned

This project helped us strengthen our understanding of full-stack integration, API configuration, deployment workflows, environment management, and the process of making a software project accessible to real users.

What's Next

We aim to improve anomaly detection, provide more detailed incident investigation workflows, and make log analysis more reliable and useful for development teams.