Inspiration

AI-Powered Real Estate Assistant

Inspiration

Real estate professionals spend significant time searching listings, reviewing documents, and answering repetitive client questions. We wanted to build an AI-native assistant that streamlines these tasks, making property information easier to access and helping users make faster, data-driven decisions.

What it does

Our platform enables users to search properties, upload and analyze real estate documents, and ask questions in natural language. By combining Retrieval-Augmented Generation (RAG) with OpenAI models, the assistant generates responses grounded in the user's own property data, helping reduce hallucinations and improve reliability.

How we built it

We built the application using Next.js for the frontend and PostgreSQL for data storage. Property listings and uploaded documents are processed into embeddings and indexed for semantic search. When a user asks a question, the system retrieves the most relevant information through a RAG pipeline and sends it to OpenAI GPT-5.5 using the Responses API to generate context-aware answers. Throughout development, we used Codex to accelerate coding, debugging, and feature implementation, allowing us to iterate much faster during the hackathon.

Challenges we ran into

One of the biggest challenges was designing a retrieval pipeline that consistently returned relevant context while keeping response times low. We also worked on improving prompt engineering, document chunking, and retrieval quality so the AI would provide accurate, trustworthy answers based on the uploaded data rather than relying solely on its pretrained knowledge.

Accomplishments that we're proud of

• Built a complete AI-native real estate assistant within the hackathon timeframe.
• Successfully integrated OpenAI GPT-5.5 with a RAG pipeline for grounded responses.
• Implemented intelligent document understanding and semantic search.
• Created a modern, user-friendly interface that simplifies property discovery and information retrieval.

What we learned

This project deepened our understanding of AI-native application development, Retrieval-Augmented Generation, vector search, prompt engineering, and the OpenAI ecosystem. We also learned how AI coding tools like Codex can significantly improve development speed without sacrificing code quality.

What's next

Our roadmap includes AI-powered property recommendations, market price analysis, multilingual support, workflow automation for real estate agents, and integrations with external property listing platforms. We also plan to introduce agentic workflows that can automate repetitive tasks such as document processing, lead qualification, and follow-up communication.

What it does

How we built it

Challenges we ran into

Accomplishments that we're proud of

What we learned

What's next for AI Native

Built With

• next.js
• openai-gpt-5.5
• openai-responses-api
• postgresql
• react
• retrieval-augmented-generation-(rag)
• tailwind-css
• typescript
• vector-search