Veritone Inc. (Nasdaq: VERI), the creator of the world's first operating system for artificial intelligence, aiWARE™, today announced the expansion of its content classification capabilities to support IAB Tech Lab Content Taxonomy-compliant metadata output via aiWARE APIs. The new offering will enable podcast distribution platforms to programmatically transcribe and tag audio streams with topical, descriptive, and time-correlated keyword metadata prior to publishing, allowing for advanced contextual ad targeting at scale. Further, greater insight into the subject matter of the podcast helps advertisers maintain brand safety by avoiding association with content that is not in alignment with their brand values.
Over 30 million podcast episodes are available today (versus 18.5 million in 2018), and consumer demand continues to increase. At the same time, consumer privacy laws are changing and severely curtailing the tracking of audience data. As a result, the ability to tag content with descriptive metadata is critical to enabling an alternative, contextual ad-targeting approach. Until now, tagging of media such as podcasts with contextual metadata has been a labor-intensive and time-consuming manual process, and it has largely been limited to program descriptions at the show and episode level, limiting a publisher's or advertiser's ability to find specific types of content for advertisement placement consideration. This manual process is not scalable to handle the ever-increasing amount of new content being produced daily and cannot support programmatic contextual ad targeting.
Veritone aiWARE's new content classification capability removes these hurdles through comprehensive and automated topic analysis, extraction, and time-coded tagging of the podcast with the applicable content category codes from the IAB Tech Lab Content Taxonomy. Podcast publishers can easily integrate the solution into their existing infrastructure and workflows.