Understanding the behaviour of complex machine learning models has become a challenge in the modern day. Explainable AI (XAI) methods were introduced to provide insights into model predictions, however explaining these explanations can be difficult without proper visualisation methods. In addition, settling the disagreements between these explainers can be difficult based purely on numerical values without a proper visual indicator of the question each method is answering. To fill this gap we propose geometric representations and methods to unify and coalesce the explanations from different explainers.