AI Software Architect

Inspiration

This project was inspired by my experience during my final-year project, where I was responsible for the entire backend, including database design. I struggled to define user types, model relationships between tables, and design a system that could scale effectively. I also found it challenging to build an architecture that could support both a web application and a mobile application while sharing a single backend and database. Those experiences made me realize how critical it is to make sound architectural decisions early in the development process, as they have a lasting impact on scalability, maintainability, and the overall success of a software system.

What it does

AI Software Architect is a tool that helps teams review software design documents before implementation begins. Instead of discovering missing requirements or architectural risks late in the process, users can upload documents such as requirements, user stories, API notes, or architecture files and receive a structured review with findings, recommendations, and meaningful follow-up questions. The goal is to turn scattered technical documents into clear, actionable architecture guidance.

How we built it

I built the project using Python and FastAPI for the backend, a simple web interface for the frontend, and AI APIs to generate architecture reviews. The system accepts uploaded documents, extracts relevant content, and produces a structured report with insights and recommendations. The design focuses on simplicity, usability, and making the experience easy to demo and understand.

Challenges we ran into

One of the biggest challenges was designing the system so it could handle a wide variety of document types while still producing reliable and meaningful analysis. I also had to make sure the solution stayed practical and easy to use, rather than becoming overly complex. Another challenge was creating an architecture that could support future expansion without sacrificing clarity or maintainability.

Accomplishments that we're proud of

We are proud to have built a working MVP that demonstrates the value of AI in architecture review. The project is able to take raw documents, generate structured insights, and provide useful recommendations that can guide teams toward better design decisions. It also shows how AI can make technical planning more accessible and efficient.

What we learned

Through this project, I learned a great deal about building AI-powered applications, processing document-based inputs, and turning raw technical content into structured insights. I also learned the importance of designing systems thoughtfully from the start, especially when working with multiple interfaces and shared backend services.

What's next for AI Software Architect

The next step is to make the platform more powerful and user-friendly by adding better deployment options, more advanced analytics, deeper architecture recommendations, and support for ongoing software projects. Rather than only reviewing requirements at the start, the platform will continuously analyze evolving requirements, architecture, and implementation decisions throughout the development lifecycle, helping teams identify risks, maintain consistency, and adapt to changes as projects grow. These improvements will enable the system to support more complex applications while providing increasingly accurate and actionable guidance for developers and engineering teams.

Built With

• api
• apis
• architecture
• assistant
• backend
• css
• development
• document
• export
• fastapi
• full-stack
• groq
• html
• javascript
• openai
• pdf
• processing
• python
• rest
• software
• sqlite
• web