Data & Innovation • Transportation & Logistics • AWS
Chatbot developed with GenAI to enhance customer experience
Chilean logistics company implements a chatbot that responds in real time to inquiries about services and shipment status.
About the client
A Chilean company with more than 25 years of experience, consolidated as a leader in logistics solutions for e-commerce and courier services. Its focus is on efficiently connecting people and businesses across the entire country, including its most remote areas. Originally founded as Lan Courier in 1996, it underwent significant evolution and adopted its current name in 2008. In 2022, it became part of the Empresas Copec portfolio, boosting its growth and expansion.
Today, the company operates a network of more than 3,200 pick-up points, including neighborhood stores and service stations, offering customers the convenience of sending, returning, or collecting packages at flexible hours. It has also introduced innovations such as smart lockers and automated shipping systems, enabling fast and autonomous processes for users. With a robust infrastructure and a strong commitment to innovation, this company continues to lead the digital transformation of logistics in Chile, delivering agile, secure, and sustainable solutions for its clients.
Needs
With the goal of improving response times and customer service quality, the company needed to incorporate Generative Artificial Intelligence (GenAI) technologies to provide automated, accurate, and real-time support for frequent inquiries and shipment tracking.
Solution
Nubiral implemented a GenAI-based Proof of Concept (PoC), developing a conversational assistant hosted in the client’s AWS environment.
This assistant was trained using information from the official website, a frequently asked questions (FAQ) document, and a simulated service order database, enabling natural language responses for general inquiries and shipment tracking.
Results
A fully functional chatbot was deployed with advanced natural language understanding and generation capabilities, able to deliver agile, contextualized responses.
Using a Retrieval-Augmented Generation (RAG) pattern, it dynamically accesses FAQs and a CSV view of shipping data, providing accurate order status updates based on order IDs.
This solution represents a solid first step toward intelligent automation of customer service in the logistics sector.
