Turn transcripts
into discourse you can code.

A different approach to discourse analysis.

Discourse analysis is the study of language in use: how a text or conversation is constructed, framed, and interpreted, not just what it says on the surface. Linguists, communication researchers, sociologists, and political scientists use it to uncover how word choice, framing, and structure shape meaning and power, across interviews, speeches, media coverage, and policy documents.

Why discourse analysis software stalls at scale

Tools like Atlas.ti, NVivo, and Dedoose have run this work for years, and each does real coding well: node structures, text search, concordance, automated first-pass coding. The limit is not the software, it is the workflow around it. Someone still transcribes the interview before it can be coded. Someone still builds every node by hand, applies the same framework transcript by transcript, and stitches the results into a report weeks later. A researcher with 40 transcripts and a real deadline runs out of hours long before they run out of data.

Reading the conversation, not just the transcript

Speak AI treats every recording the way a discourse analyst would, at machine speed. Each interview, speech, or focus group is transcribed in your language, with 100+ supported, and then the delivery itself is read alongside the words: tone, pacing, and emphasis, not just the transcript text. Your codebook, your discourse markers, and your theoretical framework are applied consistently across every transcript, and quotes, framing shifts, and speaker stance are extracted into structured fields your project can use.

Then the questions start. Ask across your entire transcript corpus with AI chat, running the same close-reading prompts a research team once repeated by hand, now native across recordings with ChatGPT, Claude, and Gemini built in.

What teams ask their discourse data

• “Which speakers frame this issue as a crisis, and which frame it as routine?”
• “Show me every passage that shifts from personal to institutional language.”
• “What discourse markers show up most often across the interview set?”
• “Which transcripts show a change in stance between the first and second half?”
• “Summarize the dominant framing across all speeches this quarter.”

From a folder of transcripts to a coded corpus

The result is a coded corpus instead of a folder of transcripts and a spreadsheet of node counts. Framing shifts surface automatically instead of on a second read-through. Patterns across dozens of interviews become a report instead of a hunch, and dashboards you can customize and white-label track framing, stance, and discourse markers over time, so this quarter’s coverage is measured against last quarter’s. One legal intelligence firm put its call and interview volume through this workflow and processed 5,100+ hours and saved $700K.

And because discourse rarely lives in one format alone, the same engine, running on Claude, ChatGPT, and Gemini, connects your transcripts to call scoring and coaching across every conversation your project has.