Industrial anomaly detection becomes challenging due to the sparsity and randomness of outlier images and environmental noise. Traditional methods suffer under such circumstances, and therefore, there must be a model with accuracy and also with the capacity for speedy classification and segmentation of the anomalies. Our solution brings into action a teacher-student network for stable classification and also an autoencoder for precise localization of the anomalies. The teacher-student network discriminates between normal and abnormal images very well, and the utilization of autoencoders reconstruct images and highlight differences. Adopting the notion of EfficientAD, we strengthen this fusion with a multi-modal approach, integrating the best of both frameworks.