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AI-Driven Real Estate Enhancement Project
#proptech, #ML/AI
CHALLENGE
The existing real estate application faced several challenges that hindered its effectiveness. These challenges encompassed suboptimal performance, inadequate user experience, and the necessity to remain competitive in a rapidly evolving real estate landscape. The absence of advanced AI-driven features further limited the application's capabilities and hindered its potential to offer cutting-edge services.
CLIENT
Leading real estate
TECHNOLOGY
Java 8, Python, JavaScript ES5-ES6, Spring 4-5, Spring Boot 2, MySQL, Elasticsearch, Hibernate, PySpark, AWS SageMaker, AWS, Tomcat, Apache HTTP, JQuery, HtmlUnit, Selenium, Maven, Jenkins.
TEAM
Dedicated team with expertise in back-end development and deep knowledge of machine learning and artificial intelligence
INDUSTRY
Proptech
SOLUTION
The project introduced a comprehensive solution to address the identified problems. It entailed a multifaceted approach:
Microservices Development: The application's architecture was revamped by implementing microservices and enhancing scalability, flexibility, and maintainability.
Performance Enhancement: The team improved the application's performance, achieving remarkable gains of up to 50% in certain services, ensuring smoother user interactions.
AI-Driven Predictive Analytics: A novel machine learning model was developed from scratch, leveraging PySpark and AWS SageMaker. This model could predict house sales, enabling users to make informed decisions based on data-driven insights.
Modernization of Technologies: The project encompassed migrating from traditional technologies like Spring 4 to more advanced versions like Spring 5 and transitioning from JDBC to Spring Data JPA. Additionally, the application shifted from SQL databases to Elasticsearch, enhancing search capabilities.
IMPACT
he project's results were transformative and impactful:
The application's performance was substantially enhanced, offering users quicker responses and improved navigation, contributing to higher engagement rates.
Introducing AI-powered predictive analytics gave users valuable insights, aiding in making informed decisions when buying or selling properties.
The modernization of technologies and migration to Elasticsearch led to a more efficient search experience, enabling users to find properties more effectively.
These enhancements culminated in an improved overall user experience, a competitive edge for the client, and a well-optimized real estate application that meets modern market demands.
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