The company’s recommendation engine starts by transcribing the podcasts that users listen to, because when it converts audio to text, its AI can mine the data to make better, more personalized recommendations. It then extracts topics from each podcast episode based on the transcripts and adds the data to a “Podcast Genome,” which is a massive database that shows how podcasts intersect.