In this study, several well-established convolutional neural network (CNN) architectures were employed to enable the automatic and reliable interpretation of Pile Integrity Test (PIT) data, with a particular emphasis on integrating soil characteristics as auxiliary input features. While previous studies have primarily focused on analyzing signal data alone, the significant impact of subsurface soil conditions on wave propagation and signal interpretation has been widely recognized by geotechnical experts. However, this critical aspect has often been overlooked in data-driven approaches. To address this limitation, we collected PIT data from 278 foundation piles constructed across sites with diverse geotechnical profiles. Each data sample was augmented with relevant soil-related features, including soil classification, stiffness parameters, and localized stratigraphic information. Among the tested CNN models, the highest classification accuracy achieved was 95%. Importantly, all data used in the study were obtained from real-world PIT measurements, as opposed to synthetic reflectograms commonly used in earlier research, thereby enhancing the practical relevance and generalizability of the results. The inclusion of soil characteristics was found to substantially improve model performance, increasing accuracy from 90% (signal-only input) to 95% when soil features were incorporated. This study represents the first comprehensive effort to explicitly include soil influence in PIT data analysis using deep learning and offers a novel contribution to AI-powered decision-support systems in structural and geotechnical engineering. The proposed soil-aware approach opens up new opportunities for more accurate and context-sensitive defect detection in pile foundations.