Sep 22, 2023
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Completed
AI-Driven Real Estate Enhancement Project
$75,000+
more 1 year
Denmark
6-9
Service categories
Service Lines
Artificial Intelligence
Software Development
Web Development
Domain focus
Real Estate
Programming language
Java
JavaScript
Python
Frameworks
React.js
Spring
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.
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.
Solution
It entailed a multifaceted approach that included:
The application's architecture was revamped by implementing microservices, enhancing scalability, flexibility, and maintainability.
The team worked on improving the application's performance, achieving remarkable gains of up to 50% in certain services, ensuring smoother user interactions.
A novel machine learning model was developed from scratch, leveraging PySpark and AWS SageMaker.
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.
It entailed a multifaceted approach that included:
The application's architecture was revamped by implementing microservices, enhancing scalability, flexibility, and maintainability.
The team worked on improving the application's performance, achieving remarkable gains of up to 50% in certain services, ensuring smoother user interactions.
A novel machine learning model was developed from scratch, leveraging PySpark and AWS SageMaker.
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.
Results
The 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.
The introduction of AI-powered predictive analytics provided users with 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.
Collectively, these enhancements culminated in an improved overall user experience, a competitive edge for the client, and a well-optimized real estate application capable of meeting modern market demands.
The 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.
The introduction of AI-powered predictive analytics provided users with 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.
Collectively, these enhancements culminated in an improved overall user experience, a competitive edge for the client, and a well-optimized real estate application capable of meeting modern market demands.