1- Each model looks for different deepfake signatures. By design, the models do not always agree, which is the goal. We are much more concerned with false negatives, and we target a min of 95% accuracy for our model of detection models. 2 - The challenge is educating users about results without requiring a PhD. Our platform is targeted for use by junior analysts in cyber security or trust and safety. 3 - This is a good suggestion. We are exploring how we can offer an unlimited plan that can cover our high compute costs (we run our multiple models in realtime).