HomeScout AI

Tech Stack Architecture
Extension Survey
MongoDB Database
Blockchain

Inspiration

We wanted to solve the real estate problem of providing highly personalized home recommendations that also prioritize privacy and data security. By leveraging the best in AI and blockchain technology, we set out to create a seamless user experience for potential homebuyers, helping them find homes tailored to their exact preferences while ensuring their personal data remains secure and private.

What it does

HomeScout AI is a next-gen real estate platform that uses AI-driven personalized recommendations to match users with the best homes available, based on their preferences. The platform integrates with CoStar's Homes.com to pull real-time property listings and leverages Azure OpenAI for intelligent, customized suggestions. At the core of the system is MongoDB, which stores all user preferences and house profiles, acting as the operational hub for data transfer between AI, blockchain, and other APIs. Privacy is ensured by using the Midnight Blockchain, which keeps sensitive user data confidential through zero-knowledge proofs.

How we built it

1. Backend Architecture:

MongoDB is the central database in our architecture. It stores user preferences, house profiles, and metadata from real estate sources like CoStar's Homes.com. Its document-based, NoSQL format allows for flexible schema design, ensuring we can easily store and update diverse datasets with user-specific and home-specific data.
- Data is categorized into different collections, such as:
  - users: Storing encrypted user profiles and preferences.
  - house_profiles: Storing the latest property data fetched from the Homes.com.
  - recommendations: Logs of AI-generated recommendations, providing a trail of what has been suggested to users.
- Data Flow: MongoDB acts as the intermediary between the front-end and other back-end components (AI engines, blockchain), handling fast data transfers and providing real-time updates.

2. AI-Powered Recommendations:

We used Azure OpenAI to develop the recommendation engine. The AI analyzes user preferences (e.g., location, number of bedrooms, house style, amenities) and matches them with real estate listings stored in MongoDB.
- AI Model: The AI model is trained on user preferences and historical matching patterns to continuously improve the relevance of the recommendations. Natural Language Processing (NLP) is used to interpret user input and refine their preferences, while machine learning algorithms are used to find correlations between house features and user satisfaction.
- Interaction with MongoDB: Every time a user inputs or updates their preferences, these are stored in MongoDB. The AI queries this database to fetch relevant profiles and recommends homes that best match these preferences.
- CoStar's Homes.com Integration: We query the Homes.com website to ensure that we provide up-to-date and accurate property listings. This API provides essential details such as pricing, amenities, location, and more, which are stored in MongoDB and used by Azure OpenAI to generate personalized recommendations.

3. Privacy and Security via Blockchain:

Midnight Blockchain is used to secure sensitive user data, such as their preferences, transactions, and interactions with the platform. By employing zero-knowledge proofs (ZK Snarks), we ensure that the system can validate the integrity of the recommendations without revealing sensitive user data.
- Ledger System: The Midnight blockchain ledger stores a public record of transactions and recommendations while keeping user-specific information encrypted and confidential. Users’ preferences are encrypted using their private keys and only decrypted during recommendation matching, ensuring complete privacy.
- Smart Contracts: We implemented smart contracts that handle the matching logic between user preferences and house profiles. These contracts operate off-chain for private operations (such as preference matching) while ensuring transparency and accountability for public operations (such as listing recommendations).

4. Frontend:

The front-end is built using modern web technologies (HTML, CSS, JavaScript) with a focus on a React.js framework. It connects to the back-end via REST APIs and WebSocket connections for real-time updates.
- Data Binding: Real-time data from MongoDB is displayed to users, allowing them to see personalized recommendations as they are updated by the AI.
- Interactive UI: Users can modify their preferences, view home profiles, and get immediate feedback from the AI engine.
- Data Privacy Display: The front-end communicates with the Midnight blockchain to inform users how their data is handled, giving them transparency and control over their private information.

Challenges we ran into

Data Synchronization: Keeping user preferences and house listings synchronized between MongoDB and the Homes.com API was a challenge, especially when handling frequent updates from both ends.
Security and Privacy: Implementing zero-knowledge proofs in the blockchain required extensive research and careful integration to ensure that the system remained both secure and performant.
Performance Optimization: Generating real-time personalized recommendations from large datasets required optimizing how data was stored and queried in MongoDB, as well as ensuring efficient communication between the AI and the database.

Accomplishments that we're proud of

Successfully building an end-to-end system that integrates AI, blockchain, and real estate APIs to provide personalized home recommendations while ensuring privacy.
Using MongoDB as the heart of the platform, efficiently managing the complex interactions between AI, blockchain, and the frontend in real-time.
Deploying the platform with Azure OpenAI and Midnight Blockchain to ensure both personalization and security in a seamless manner.

What we learned

AI Optimization: We learned how to fine-tune AI models to interpret user preferences and produce accurate, real-time recommendations.
Database Management: MongoDB’s flexible schema design allowed us to iterate quickly, storing user and property data in a scalable way.
Blockchain and Privacy: We deepened our understanding of integrating blockchain technology into traditional applications to secure data, especially with zero-knowledge proofs and smart contracts.

What's next for HomeScout AI

Expand AI Capabilities: We plan to enhance the AI model's ability to handle even more complex user preferences and interactions, such as understanding nuanced preferences through conversational AI.
Broaden Real Estate Data Sources: We aim to expand beyond CoStar's Homes.com by integrating other property databases to increase the variety of homes available.
Mobile App Development: Developing a mobile version of the platform to make it more accessible to users on the go.
Advanced Blockchain Features: We will further explore decentralized identity solutions to enhance user authentication and increase control over personal data using blockchain technology.