Podcast Intelligence & Transcripts

Search What Was Said, Not Just What Was Published

Full transcripts, speaker identification, and topic tagging across a growing library of podcasts by category. Built for teams who need podcast content in a structured, queryable format rather than an audio file.

Request Access See a Sample Dataset

An audio file isn't data — it's a black box until someone listens to it. Subalytics turns podcast episodes into structured intelligence: full transcripts, speaker identification, and topic tagging across a growing library organized by category, so what was actually said becomes something you can search, filter, and query, instead of something you have to sit through.

From Audio File to Structured Dataset

What 'Structured' Actually Means Here

Full transcripts

Every tracked episode is transcribed in full, timestamped and ready to search.

Speaker identification

Segments are labeled by speaker, so you can isolate what a specific host or guest said, not just the episode as a whole.

Topic tagging

Episodes and segments are tagged by topic, so you can find relevant content by subject, not just by show name.

Category organization

The podcast library is organized by category, so discovery starts from the topic or industry you actually care about.

Built for Querying, Not Just Reading

['Most transcript tools produce a text file meant to be read start to finish, once. Subalytics is built around the opposite assumption: that the real value is in querying across many episodes at once — find every mention of a topic, isolate what one speaker has said across dozens of appearances, or pull every segment tagged to a category.', "That structure is what makes podcast content usable as a dataset, not just a readable transcript — whether you're pulling it into a dashboard, a research workflow, or your own analysis pipeline."]

See API Access for Podcast Data →

A Growing Library, Organized by Category

Coverage expands by category over time, so you can go deep in the industries and topics that matter to your work rather than getting shallow coverage spread thin across every possible show. If a category you need isn't yet covered, tracked shows can be added to the pipeline directly.

FAQ

Common questions

What does 'structured' podcast data mean, exactly?+

It means each episode is broken down into a full transcript, speaker-labeled segments, and topic tags — rather than existing only as an audio file or a single block of unstructured text.

Can I search for what a specific speaker or guest has said across multiple episodes?+

Yes — speaker identification means you can isolate a specific person's segments across every episode they've appeared in, not just within a single show.

How is this different from the podcast transcript feature?+

Transcription is the underlying layer — this use case adds speaker identification, topic tagging, and category-based organization on top of it, so the data is structured for querying and analysis, not just for reading a single episode's text.

Is podcast data available via API?+

Yes — teams that want to pipe transcripts, speaker data, or topic tags into their own tools or pipelines can access it through the API.

How is the podcast library organized?+

By category, so coverage can go deep within specific industries or topics rather than spreading thin across every possible show.

Have a question or a custom use case?

Tell us what you're trying to monitor or search across, and we'll get back to you within a business day.