Customer service is currently of great importance because this process supports the management of customer queries, requests and disagreements. In customer service, noncompliance with resolution times as well as closing times appearsas a recurring problem, which has a direct relationship with the customer experience. An alternative to improve this process isthrough the use of natural language processing algorithms to facilitate the complaint of customers considering platforms and solutions already applied in the market. In this paper, a customer complaint classifier is proposed using Google's Dialog Flow system in a Chilean company considering the implementation of a prototype composed of a text classification model and a system that manages customer interactions. This system allows an automatic categorization of customer interactions, allowing the tasks of a customer service representative to be reduced, minimizing the risks of misclassified interactions and avoiding re-categorizations that increase resolutiontimes. The proposed model allows improvements over 50% accuracy over the classification given by the pre-existing system based on user feedback. As future work we propose to create a chatbot system that allows improving user feedback.