Case Study - LLMs for customer service optimization
LLMs help streamline customer service operations
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Summary
New Gradient developed a custom AI solution using Retrieval-Augmented Generation (RAG) and Large Language Models (LLMs) to optimize product-based customer service for Omlet, a major online pet products e-commerce company. This innovative approach significantly enhanced customer service efficiency
LLMs and RAG for customer service
E-commerce stores face a considerable challenge in handling the extremely high volume of product-related questions they receive. These queries span a wide range of issues, from use case inquiries to manufacturing defects. Managing this volume typically requires a large team of customer service staff. However, even with a substantial team, addressing specific questions can be challenging due to the vast and diverse product base, which often includes niche items requiring specialized knowledge.
The implementation of our AI-powered customer service solution for Omlet demonstrates New Gradient's ability to apply advanced AI technologies to solve real-world business challenges. By enhancing the efficiency and accuracy of customer service operations, our solution not only improves the customer experience but also reduces operational costs for Omlet. This project showcases our expertise in developing practical AI applications that can transform traditional business processes, particularly in the fast-paced e-commerce sector.
The system's ability to quickly access and process vast amounts of product-specific information allows it to handle a wide range of queries effectively. This significantly speeds up the job of customer service agents, enabling them to process many more customer queries efficiently and accurately.
We developed a sophisticated RAG and LLM-based solution tailored for Omlet's customer service needs. Our approach utilized Omlet's entire backlog of previous customer service correspondences, product information sheets, customer service documentation, FAQs, and other relevant data sources. We employed vector embeddings and retrieval augmented generation techniques to efficiently retrieve information relevant to customer queries. This was combined with fine-tuned LLMs to provide factual and appropriately toned answers to customer inquiries.
