ArchitectAI

AI Software Engineer for Codebase Analysis Upload any software project and instantly understand its architecture, technologies, and code using AI.

Comment

ArchitectAI – AI Software Engineer for Codebase Analysis

Inspiration

As students and developers, we often spend a lot of time trying to understand unfamiliar codebases. Whether it's an open-source repository, a college project, or a team project, understanding how everything is connected usually requires reading dozens or even hundreds of files.

We wanted to simplify that process by building an AI-powered assistant that can quickly analyze a software project and explain it in a way that's easy to understand. Our goal was to create a tool that helps developers spend less time exploring code and more time building software.

What it does

ArchitectAI is an AI-powered web application that analyzes software projects.

Users can upload a project as a ZIP file, and the application automatically scans the project to detect programming languages, frameworks, folder structures, and project statistics. It then uses Google Gemini to generate a human-readable summary of the project.

The application also allows users to ask questions about the uploaded project through an AI chat interface, helping them understand project architecture, technologies used, and the overall workflow. Additionally, it provides a simple architecture visualization and can generate a professional README based on the analyzed project.

How we built it

We built ArchitectAI using a modern full-stack architecture.

The frontend was developed with React, Vite, and Tailwind CSS, creating a clean and responsive dashboard for uploading projects, viewing analysis results, and interacting with the AI assistant.

The backend was developed using FastAPI, which handles project uploads, ZIP extraction, project scanning, and API communication.

For the AI capabilities, we integrated Google Gemini, which generates project summaries and answers user questions based on the extracted project information.

Our project scanner analyzes the uploaded files, detects technologies such as programming languages and frameworks, calculates project statistics, and prepares the information before sending it to Gemini for intelligent responses.

Challenges we ran into

One of the biggest challenges was handling different project structures. Every project is organized differently, making it difficult to design a scanner that works across multiple programming languages and frameworks.

Another challenge was preparing meaningful context for the AI. Instead of sending the entire project to Gemini, we had to extract the most relevant information so the responses remained accurate and useful.

We also focused on creating a clean and simple user interface that presents technical information in an easy-to-understand way without overwhelming users.

Accomplishments that we're proud of

We're proud that we successfully built a working platform that combines project analysis and AI into a single application.

Some achievements include:

  • Successfully integrating Google Gemini for intelligent project explanations.
  • Building an automated project scanner that detects technologies and project information.
  • Creating an AI chat interface that answers questions about uploaded codebases.
  • Designing a clean and user-friendly dashboard for developers.
  • Generating project summaries and README documentation automatically.

Most importantly, we transformed a complex development problem into a simple workflow that anyone can use.

What we learned

Building ArchitectAI helped us improve our understanding of both software engineering and AI integration.

Throughout this project, we learned about:

  • Building scalable REST APIs using FastAPI.
  • Developing modern web interfaces with React and Tailwind CSS.
  • Integrating Large Language Models through the Google Gemini API.
  • Project structure analysis and automated metadata extraction.
  • Prompt engineering for more accurate AI responses.
  • Designing developer-focused tools with usability in mind.
  • Connecting frontend and backend systems effectively.

We also learned the importance of balancing technical functionality with a simple and intuitive user experience.

What's next for ArchitectAI

Although the current version already provides AI-powered project understanding, we have several ideas for future improvements.

Our roadmap includes:

  • Direct GitHub repository analysis.
  • Advanced dependency and architecture visualization.
  • Security vulnerability detection.
  • Code quality and technical debt reports.
  • AI-powered refactoring suggestions.
  • Support for more programming languages and frameworks.
  • Team collaboration features.
  • IDE extensions for Visual Studio Code and JetBrains IDEs.

Our long-term vision is to make ArchitectAI an intelligent software engineering assistant that helps developers understand, document, and maintain projects of any size more efficiently.

Built With

Share this project:

Updates

Submission history