AI-Powered E-commerce Comparison Chatbot

Locoshop seeks to develop an AI-powered chatbot designed to enhance the online shopping experience by comparing products and prices across multiple online stores. The goal of this project is to create a user-friendly tool that assists customers in making informed purchasing decisions by providing real-time comparisons. This project offers learners the opportunity to apply their knowledge of artificial intelligence, natural language processing, towards interpreting data extracted from e-commerce systems. The chatbot should be capable of understanding user queries, retrieving relevant product information from our database, and answering queries in a clear and concise manner. The project will focus on integrating various AI models to ensuring the chatbot's responses are natural, accurate and timely. This initiative will help Locoshop position itself as a leader in innovative shopping solutions.

Quick browse template for e-commerce websites

Project Overview: The goal of this project is to develop a web application that generates mobile-friendly templates for easier browsing of e-commerce websites on mobile devices. The app will allow users to enter the URL of an e-commerce website, extract product data, and use this data to populate a mobile-optimized template to browse the entire product catalog. This template can then be shared on social media or embedded in other websites as a quick-browse option. Project Objectives: To design and implement a user-friendly web app. To facilitate easier mobile browsing for any e-commerce website. To provide a seamless registration and login experience. To enhance online shopping user engagement through optimized mobile presentation.

Product matching algorithm for Locoshop

Locoshop aggregates product data from millions of e-commerce websites globally, enabling consumers to efficiently find the products they seek. However, we face a significant challenge: accurately matching identical products when the product information varies across different stores. Currently, we utilize Elasticsearch with keyword fuzziness to match products. This method depends heavily on the similarity of product information entered by different stores, leading to limitations in accuracy. To enhance our capability to match identical products with high certainty, we need to develop more sophisticated matching techniques. This is crucial, especially under the following conditions: Product descriptions vary significantly from store to store. Product images differ across stores. UPC (or Manufacturer Product ID) codes are not consistently available. We are exploring advanced solutions, including machine learning algorithms that can analyze images and text to recognize products regardless of description variability, and data enrichment techniques to compensate for missing information like UPC codes. This will enable us to meet our desired use cases with the required accuracy.