<p>Identifying digital platform companies has proven to be a formidable task due to the intricate nature of these organizations. This complexity often results in incomplete depictions, exacerbated by the inherent tendency of digital platforms to blur traditional sectoral boundaries. To bridge this knowledge gap, we propose an innovative methodology that harnesses the power of Natural Language Processing (NLP) techniques for the systematic identification of digital platform companies on a global scale. Moreover, we present an applied exercise aimed at creating a comprehensive world map that precisely locates these platform companies. Our approach and exercise offer four distinct contributions: (i) our methodology validates an artificial intelligence algorithm-based approach for identifying companies based on the products and services they offer. This not only enhances the accuracy of our identification process but also sets a precedent for the application of AI in this context; (ii) by facilitating the identification of digital platform firms, our methodology empowers researchers in the fields of business and economics. This empowerment enables a more precise and comprehensive understanding of the intricacies of the platform economy, thereby facilitating in-depth research and analysis; (iii) our findings provide invaluable insights for policymakers who grapple with the complexities of the platform economy. These insights serve as a crucial tool for crafting effective regulations and fostering healthy competition within the digital marketplace, ultimately benefiting consumers and businesses alike; (iv) through the visual representation of platform company distribution on our map, we offer a tangible means to test and refine existing theories regarding how these companies operate and thrive in various regions. This empirical validation contributes to the advancement of platform geography theories, particularly those related to value creation and appropriation.</p>