ProductOps embarked on a pioneering project with a leading global bicycle manufacturer, leveraging AI technologies to enhance customer service via a Retrieval-Augmented Generation (RAG) application.. The aim was to create a chat app that delivers immediate self-help to customers, potentially reducing the need for help-desk tickets and bypassing the limitations of traditional decision-tree based chatbots.

Faced with the challenge of managing an expanding global customer base and increasingly complex queries, the bicycle company sought to leverage AI to streamline customer service. The solution was an AI-powered multilingual customer service chatbot developed using a Flask framework and the Mistral 7B Large Language Model (LLM), along with other technologies like Ollama, Hugging Face embeddings, and LangChain. This setup was chosen for its cost-effectiveness, flexibility, and ability to quickly prototype a proof of concept.

The chatbot was designed to understand queries in multiple languages, retrieve relevant information, and generate accurate responses, using data such as bicycle owner manuals and technical guides ingested into the Chroma Vector DB primarily in English. Multilingual support was crucial, with tests conducted in English, French, Italian, German, and Spanish, demonstrating the application's global applicability. Hosting the model in a private cloud was a strategic decision to control costs and performance.

The project's success hinged on its ability to answer questions in the supported languages directly through the chat, significantly reducing the volume of help-desk tickets by an estimated 30%. This reduction was attributed to the chatbot's capacity to provide instant, precise answers, often resolving issues without human intervention. The multilingual support was a key factor in enhancing customer satisfaction and loyalty globally, showcasing the transformative potential of AI in improving customer service experiences.