This chapter applies theAutoencoder autoencoder-based attribution framework developed in Chap. 5 to a series of historically significant and methodologically diverse case studies in late Georgian porcelain decoration. It integrates traditional connoisseurshipConnoisseurship, documentary provenanceProvenance, and quantitative computational analysis to assess both securely attributed works and long-standing attribution controversies associated with William Billingsley and his contemporaries. Through detailed examination of services such as the Prince of Wales, Earl Camden, Duke of Northumberland, and Pendock BarryPendock Barry services, the chapter demonstrates how reconstruction error metrics and similarity indices can distinguish between closely related artistic hands under controlled conditions. Comparative analyses, including direct contrasts between Billingsley and Edward WithersWithers, Edward, validate the discriminative power and specificity of the approach. The chapter emphasises attribution as a probabilistic process and illustrates how convergence between historical evidence, stylistic analysis, and machine learningMachine learning outputs strengthens confidence in attribution while responsibly acknowledging residual uncertainty.