The goal of this thesis is to facilitate the adoption of animals from shelters and reduce the risk of post-adoption abandonment by enabling more conscious and compatible adoptions. To address this challenge, a mobile application built with Flutter and a web platform built using HTML, CSS, and JavaScript have been developed, both sharing a common backend implemented in Django. The core of the application is a recommender system computing compatibility scores to suggest the most suitable matches between potential adopters and animals. The recommendation mechanism requires defining users' and animals' profiles. For this purpose, the platform collects data through questionnaires: users provide information about their lifestyle, habits, and home conditions, while shelter volunteers describe the behavioral characteristics and needs of the animals.