Clothiq: One Stop Platform for Clothing Recommendations
Over the last few years, online shopping has taken a boom in Pakistan, but it has also created problems for both the buyer and the seller. With such a large number of online stores and platforms, one may find it challenging to identify the most suitable clothing item. Also, most e-commerce platforms and stores rely primarily on keyword-based search engines, which can produce inappropriate or incorrect results, particularly when consumers search in common language. For example, if someone searches for a “Blue Shalwar Kameez with a front pocket”, they may receive inaccurate results since the search engines struggle to understand natural language queries and rely heavily on specific keywords. This divergence frustrates shoppers and reduces the overall efficiency and enjoyment of the online purchasing process. On the other hand, sellers also face many challenges. One such challenge is low visibility of their products that they sell due to inefficient search optimization algorithms, which leads to poor sales and missed opportunities.

This project presents the development and implementation details of ”Clothiq”, an intelligent system that provides a one-stop platform for people to search clothing items online. Traditional online clothing search often requires users to manually navigate various websites, a time-consuming and inefficient process. This project proposes a novel approach utilizing Large Language Models (LLMs) in conjunction with Retrieval-Augmented Generation (RAG) to streamline seller recommendation for specific clothing items. The system will showcase product listings from nearby active sellers, allowing users to seamlessly access the desired product with a single click. This innovative solution has the potential to revolutionize online clothing search, offering a faster and more efficient way for users to find the items they desire.
