- Infrastructure as a Service (IaaS) - Cloud Middleware & Microservices
- Platform as a Service (PaaS) - Application Development Platforms
- Oil & Gas
- Retail
- Maintenance
- Sales & Marketing
- Behavior & Emotion Tracking
- Retail Store Automation
- System Integration
- Testing & Certification
The client is a leading luxury store chain with a significant presence in North America and across the globe. They retail exclusive clothing, accessories, and home products to customers in over 100 countries. The client operates an online platform to cater to the shopping needs of their customers. They were seeking to improve the shopping experience and increase sales on their online platform by making it easier for customers to find the right products.
The client, a leading luxury store chain with a global presence, was facing challenges with their online platform. They wanted to enhance the shopping experience and boost sales by making it easier for customers to find the right products. The existing search engine on their online shopping website was not meeting the client's speed and accuracy requirements. Furthermore, the client was unable to customize the search engine to effectively match their evolving needs. The client was in need of a new, flexible solution that could improve the customer experience on their online platform.
N-iX developed a new, highly accurate, and intuitive search engine for the client's online platform. The solution comprised two services: the Search service and the Configuration service. The Search service, available on both the client's website and mobile application, used the main search index and two additional indexes stored in Elasticsearch to quickly find information. The Configuration service was a separate platform that included a list of synonyms, URL redirections based on search requests, and mappings of search phrases to specific products, brands, and categories. N-iX also developed an intuitive UI for the Configuration service. Post-implementation, N-iX continuously monitored the search results, compared them to the results of the previously used search solution, and implemented search engine improvements. They also implemented a testing script that could send up to 400 most popular search requests and gather results in an HTML report. The solution also used the CLIP neural network, Spark pipeline, Lambda, and Redis cache to improve accuracy and speed. Automation testing was established as a service to ensure system non-functional requirements for performance were met.
Case Study missing?
Start adding your own!
Register with your work email and create a new case study profile for your business.