Estator

New dreams, new beginnings, new homes, all in your hands, we not only show what it is, but what it can be.

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Inspiration

Recently, Arnav's family started looking for homes. Looking at staging costs, thinking about how to present the house, spreading information about the house, and looking for new houses was too much stress. Due to this, Arnav was motivated to create a real application for the real estate problem in Canada right now. Furthermore, Rohan's dad is a Real Estate Agent, looking for easier ways to post listings for his clients. These reasons, along with current real estate pricing margins, inspired us to create an app to rationalize and make real estate prices more reasonable.

What it does

Estator is an AI-powered platform designed to modernize property listing provided by clients, as well as the discovery processes. The core features of Estator include: Interactive Listing Browser: A map-based search interface for browsing the Chicago area real estate, along with available image editing in the listings presented. Generative AI Enhancements: Tools provided to users to improve property photos, including AI-powered decluttering (removing mess/unwanted objects), virtual redecoration (swapping furniture to preview looks), and lighting corrections. AI Listing Builder - Step-by-step tool that analyzes uploaded images of houses as well as inputted information from users to generate a professional property description and listing that is available to be networked.

How we built it

Our platform is based on a simple web stack. Frontend: Built with React as well as Framer Motion for smooth and premium animations. Image Intelligence: Fully powered by Cloudinary, utilizing its smart Generative AI capabilities for on-the-fly image transformations as well as object removal. Maps & Database: Uses Leaflet & React-Leaflet for the interactive map, and the Repliers API for real-time real estate listing data.

Challenges we ran into

Managing requests between Cloudinary and Repliers and concurrently running both of them smoothly while providing real-time feedback to the user during AI transformations was complicated at the start. Optimizing the Leaflet map to handle numerous markers and interactive UI components without sacrificing its response times and functions. Implementing sample data to ensure that the application remains functional even when API keys are missing or different limits might be reached.

Accomplishments that we're proud of

Creating a seamless AI user experience and successfully integrating Generative AI Image Creation into a real estate workflow, where users can view the potential of a property. Creating an interface that feels like it's a top-tier industry tool. The slider for the image on the main page, although it may seem silly, was something we struggled with heavily, and figuring it out was like finding the needle in a haystack.

What we learned

Generative AI: We learned about the potential of Generative AI and how it can be successfully used to solve practical industrial problems, such as the virtual staging and photo "cleanup" we created. Management/Efficiency: We learned to manage complex multi-step processes, as well as manage multiple different interfaces while keeping progress steady. Responsive Mapping: Since it was our first time using maps, we gained insights into the interactive map-based dashboards that function smoothly on various screen sizes.

What's next for Estator

Adding authentication so real estate agents or users can save or come back to favorite listings and keep a history of their AI interactions and image editing. Moving beyond just Chicago, creating databases for more places to quantify this project and reach a lot more people. Developing a dedicated mobile app for agents and users to capture and enhance photos directly on-site. Generating an immersive walkthrough of a house using VR and AI-staged imagery.

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