In this paper, we present Feature Space Navigator, an interactive interface that allows an exploration of the decision boundary of a model. The proposal aims to overcome the limitations of the techno-solutionist approach to explanations based on factual and counterfactual generation, reaffirming interactivity as a core value in designing the conversation between the model and the user. Starting from an instance, users can explore the feature space by selectively modifying the original instance, on the basis of her own knowledge and experience. The interface visually displays how model predictions react in response to the adjustments introduced by the users, letting them to identify relevant prototypes and counterfactuals. Our proposal leverages the autonomy and control of the users that can explore the behavior of the decision model accordingly with their own knowledge base, reducing the need for a dedicated explanation algorithm.