  This study investigates different approaches to classify human interactions
in an artificial intelligence-based environment, specifically for Applus+
IDIADA's intelligent agent AIDA. The main objective is to develop a classifier
that accurately identifies the type of interaction received (Conversation,
Services, or Document Translation) to direct requests to the appropriate
channel and provide a more specialized and efficient service. Various models
are compared, including LLM-based classifiers, KNN using Titan and Cohere
embeddings, SVM, and artificial neural networks. Results show that SVM and ANN
models with Cohere embeddings achieve the best overall performance, with
superior F1 scores and faster execution times compared to LLM-based approaches.
The study concludes that the SVM model with Cohere embeddings is the most
suitable option for classifying human interactions in the AIDA environment,
offering an optimal balance between accuracy and computational efficiency.
