Dexa AI
Product information, use cases, and access for Dexa AI.
Pricing information
No verified public pricing is available yet.
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What is Dexa AI?
Dexa AI is a search tool for podcast content, helping people find discussions related to a question, topic, or keyword across a large collection of shows. It is useful for listeners who want to locate worthwhile segments before committing to a full episode, as well as for research and industry-oriented exploration.
Users can ask about a specific host or search for a topic across shows, with results presented as AI-generated summaries. Timestamp links connect the overview back to relevant points in the podcast, making it easier to inspect the underlying discussion. By combining discovery, summarization, and a path to the source audio, Dexa AI supports podcast listeners, researchers, and professionals who need to screen spoken content efficiently.
Key features of Dexa AI
Podcast content indexing
Dexa AI indexes podcast content so users can search within a broad body of spoken material instead of relying only on show titles or browsing episodes one by one. This is useful when someone needs to locate a topic across multiple programs and narrow an initial research set.
Host and cross-show search
Users can ask questions about a particular host or search for a topic across different shows. This supports both focused exploration of one host’s episodes and broader discovery of how a subject appears across multiple programs.
AI-generated summaries
Search results are presented with AI-generated summaries that surface the main points of relevant podcast content. This helps users screen candidate episodes or segments quickly before deciding which underlying material to listen to in full.
Timestamp navigation and voice queries
Timestamp links let users move from a summary to the relevant point in a podcast, reducing the need to search manually through a long episode. The available product description also identifies voice input, allowing users to ask or search by voice and then review the returned podcast results.