This paper presents the development of a functional tool for the automatic detection and analysis of argumentative structures in Spanish academic texts. The system integrates a Conditional Random Fields (CRF) model trained on the CATyPI corpus, which consists of annotated thesis excerpts labeled with BIO tags for argumentative components. The model achieved a macro F1-score of 0.5896 and an accuracy of 0.657 using a context window size of three tokens, proving effective for identifying premises and conclusions. The system also incorporates a REST API developed with FastAPI and a user interface that visualizes the tagged text and offers real-time suggestions. These suggestions are generated by the ChatGPT 3.5 Turbo model based on the CRF model’s output to support academic writing improvement. The interface highlights argumentative elements and provides feedback that helps users refine their texts. Evaluation of the tool shows that it performs well in real use cases, although it still faces challenges with ambiguous or implicit argumentative structures. The project is publicly available through a GitHub repository and an interactive demo hosted on Hugging Face Spaces. This work demonstrates the feasibility of combining machine learning models with large language models to support argumentation in educational contexts, particularly for Spanish-speaking users.