Video recordings are essential for primatologists studying behaviors like social grooming, but manual annotation is a time-consuming hurdle. Our presentation showcases an automated pipeline using DeepLabCut (DLC) for pose tracking and a support vector machine to detect grooming interactions. We will discuss how our method accurately identifies grooming and turn-taking with over 86% accuracy in natural settings, uncovering specific pose features that facilitate these classifications. This breakthrough not only enhances our understanding of chimpanzee social behavior but also opens new avenues for analyzing various social interactions efficiently. During our talk we will brainstorm about the possibilities these technologies bring to the field of primatology and deep dive into the pros and cons of using them. Join us to explore how this technology is transforming behavioral research, providing deeper insights into the social dynamics of our closest living relatives and, ultimately, into our own behavior.