Proximate

Inspiration

We wanted to make finding housing less stressful and more welcoming for all students, especially those from different backgrounds or languages.

What it does

Proximate understands what students really care about, such as commute times, quiet hours, safety, budget, and their language or cultural preferences. It connects them with neighborhoods and housing options that meet those needs. Instead of only showing price and location, the app uses AI to interpret natural language input, even in different languages. It gathers real-time or demo data and provides students with smart recommendations through a clean, swipeable interface.

How we built it

We built it using Python FastAPI for the backend, a React frontend, Axios for API calls, Databricks to store the housing options, and OpenAI/Google Gemini to make the search smarter and more interactive.

Challenges we ran into

Getting the frontend and backend to sync, setting up Databricks properly, and making sure API keys and environment variables worked together took more debugging than expected.

Accomplishments that we're proud of

We’re proud that we connected a working full-stack app with clean UI, real data flows, and an AI layer that actually understands student preferences.

What we learned

We learned how to design a full-stack architecture , how to use FastAPI with React, and how tools like Axios and Databricks simplify real-time database work.