Podcasts became the new audio medium and the popularity doesn’t seem to be slowing down. There are plethora of situations where people want to consume entertainment or learn new things where visual medium isn’t applicable. Countless hours of podcasts are consumed every day by people exercising, traveling or doing household chores. Traditional news media have also jumped on the audio experience hype by offering their news articles to be listened. One of the (positive) challenges is that there such a vast amounts of content. How do you find the most interesting audio content for you? What if you want to take up a new topic and find the next favorite contents? When your podcast ends, but you still have 45 minutes to run or drive, how do you search quickly for the next interesting episode? In this project we want to explore the possibilities to automatically create user preferred audio experience. As a context we are going to use YLE’s (National broadcasting company) huge content archives. Let’s imagine a scenario where a user wants to re-visit the Paris Olympics or learn about the Second World War. How would the user go about to create a nearly endless playlist from the YLE’s content archives from the given topic by mixing podcasts, old radio shows, news audio articles, audio books, etc.? How can we help the user to dive deeper and deeper into the topic and explore the rabbit holes and interesting nuances of the topic? If you enjoy and consume lots of audio content, are interested in AI or are interested in the future of content creation, this project is for you! Join us and let’s come up with ground-breaking concepts and demos to create novel value for the audio experience!