The project starts with the knowledge of the possible problems that can affect customer service and the questions necessary to detect such a problem. Based on this training data, supervised machine learning techniques (such as probabilistic, non-probabilistic models, deep learning...) have been used to obtain the optimal way to ask questions, self-orchestrating the order of their evaluation, allowing for the most optimal solution for symptomatic and mixed user stories. This contributes new technical improvements to the company's previous developments in the field of technological solutions while providing a more secure environment through support tools.

The incorporation of Artificial Intelligence techniques as the main technology, specifically using machine learning methods (ML), has allowed for improved efficiency as the main objective of the SCHAMAN product. It is based on detecting problems presented in customer service, minimizing risks of various severities that may affect its proper functioning, and improving the quality experience for end users of advanced telecommunications services.

The result achieved represents a significant advance and contribution to one of the most widely implemented mechanisms in the business world, which is work organization through workflows. This is done through new techniques for generating, preparing, and adapting the dataset used by pre-existing AI algorithms, abstracting their capabilities and developing those non-existent for the generation of the new algorithm. It efficiently and accurately classifies a wide range of customer interactions, improving response times and the overall experience and satisfaction of customers. At the same time, it can identify emerging patterns of customer service complaints. This is a highly scalable and adaptable solution to different types of complaints and variations in the language used by customers, providing a solid foundation for future expansions.