Content creators require more than traditional search methods to meet the demands of speed, accuracy, and relevance. This presentation explores a transformative case study at Avid’s Advanced Research and Development (RAD Lab), where Artificial Intelligence (AI) and Machine Learning (ML) have been employed to overhaul traditional search mechanisms through semantic search capabilities. Leveraging AI-driven technologies, the project has established a more efficient, accurate, and cost-effective method for searching and retrieving media content. The presentation will begin with an overview of the shortcomings of conventional search techniques that rely heavily on manually tagged metadata. It will contrast these with the advanced AI/ML approaches that utilize semantic indexing to understand and interpret media content’s contextual and cultural nuances. The core objectives of this initiative were to enhance the search functionality, improve retrieval accuracy, and reduce operational costs. The project’s planning and implementation phases are outlined, demonstrating the integration of Avid’s established ecosystem components—Nexis, Elasticsearch, and Cloud-UX —culminating in a seamless transition from a proof of concept to a fully functional demo. We will discuss Avid’s engagement with Canada’s Mitacs program, which provides funding to support research internships for graduate students and postdoctoral fellows in collaboration with industry partners. Key outcomes and performance metrics will be shown, highlighting the project’s achievements in accuracy, time efficiency, and cost-effectiveness. A live demonstration will showcase the enhanced semantic search capabilities within the Cloud-UX platform. Finally, the presentation will address the challenges faced during implementation, celebrate the successes achieved, and outline the future roadmap, which includes scaling the solution and extending its capabilities to additional media types.