Deep convolutional Gaussian processes incorporate convolutional structure into the Gaussian process architecture, which allows for the extraction of local features and patterns in images. This structure enables the model to learn more complex and hierarchical representations of the input data, leading to improved performance in image classification tasks. In a company case study, the application of deep convolutional Gaussian processes for image classification on the MNIST and CIFAR-10 datasets resulted in a significant improvement in classification accuracy, particularly on the CIFAR-10 dataset, where accuracy was improved by over 10 percentage points.