Despite technological advances, precise positioning within buildings remains a considerable challenge. In this context, the present paper explores the research of user location in indoor spaces, embracing object recognition models executed directly on mobile devices. Our proposal is based on designing a generic solution architecture adaptable to any physical environment, enabling the definition and usage of relevant generic objects within the environment to determine the users' current location. This proposal uses Computer Vision, employing object recognition models for positioning. This kind of indoor positioning benefits from the growth of smartphones' functionalities and capabilities, thus avoiding the need to install additional infrastructures in physical spaces. A specific implementation of this architecture for React Native is presented, using the TensorFlow platform to support object recognition. This implementation allows demonstrating how this positioning works through concrete use cases. In addition, some lessons learned are discussed, which we hope will contribute to this topic.