Modulai built an easily integrated shopping assistant that lets site visitors ask questions about a product catalog in natural language and get relevant answers in real time, drawing on feedback from several e-commerce platforms.
Challenge
Online shoppers often want quick, specific answers about products, and finding the right item in a large catalog through search and category browsing alone can be slow. Traditional customer service does not scale to handle that volume of individual questions. An assistant that can understand a shopper's question and navigate the catalog directly addresses that gap.
Solution
Modulai built a shopping assistant on top of LangChain and large language models. It interprets a customer's question in natural language and returns relevant answers along with links to suggested products. It is designed to integrate across different e-commerce platforms without heavy setup.
Tools
The assistant was built using LangChain and large language models. It is designed for integration across multiple e-commerce platforms, analyzing a query and returning relevant answers and product links in real time.
Value created
The assistant gives e-commerce visitors a way to ask about products in plain language and get answers and product links back in real time, without browsing through search and categories. Built to integrate across different platforms with minimal setup, it lets a store offer catalog-aware support that scales to as many simultaneous questions as needed.
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