Existing search solutions are text based and they treat video as an opaque object. These search solutions index the video metadata such as title, description, etc. This approach is limiting as the full potential of what the video has to offer is not unleashed. Azure Video Indexer enables extraction of human understandable, time-stamped metadata and by building search indexes based on that metadata, a more powerful search solution can be provided to consumers. For example, indexing spoken words and faces can enable the search experience of finding moments in a video where a particular person spoke certain words or when two people were seen together. Search based on such insights from videos is applicable to news agencies, educational institutes, broadcasters, entertainment content owners, enterprise LOB apps and in general to any industry that has a video library that users need to search against.