Turn every mode
into evidence you can cite.

A different approach to multimodal discourse analysis.

Multimodal discourse analysis studies how meaning gets made across more than one channel at once: the words people use, the tone and delivery behind them, and the images, gestures, and visual staging around them. Researchers in communication, media studies, education, and marketing have used it for years to understand how an audience actually receives a message, not just what was said.

Why manual multimodal coding breaks down

The practice rarely matches the promise. A researcher watches a video once for the transcript, again for tone, and a third time for gesture and framing, coding each pass by hand into a spreadsheet. Coding drifts between sessions and between coders, and the visual and vocal layers that carry half the meaning end up reduced to a note in the margin.

Reading every mode, not just the transcript

Speak AI treats a recording the way a trained coder would, at machine speed. Each file is transcribed in your language, with 100+ supported, and then the recording itself is analyzed: the tone, pacing, and energy in the voice, and where video is available, framing, gesture, and visual cues alongside it. Your codebook becomes structured fields applied consistently across every file, so the words, the voice, and the visuals are coded together instead of three separate passes.

Then the questions start. Ask across your entire corpus with AI chat, running the same close-reading prompts you would apply by hand, now native to your recordings, with ChatGPT, Claude, and Gemini built in.

Questions researchers ask their data

• “Where does the tone of the speaker contradict what they’re saying?”
• “Which participants use hedging language, and where does their body language shift?”
• “Show me every clip where the visual framing and the spoken claim don’t match.”
• “Code every session against this framework, mode by mode.”
• “Summarize how gesture and tone change across the interview when pricing comes up.”

From raw footage to a citable dataset

The result is a coded corpus instead of a folder of raw files. Coding stays consistent across coders and sessions instead of drifting from file to file, and dashboards you can customize and white-label track theme and mode frequency over time, so this quarter’s dataset is measured against last quarter’s. One legal intelligence firm ran large-scale comparative analysis across 5,100+ hours of calls and saved $700K+, without adding headcount, the kind of scale a multimodal study can now reach too.

And because a study rarely stops at one recording type, the same engine scores calls, meetings, and interviews on the same framework, connecting your coding to call scoring and queryable through the MCP server from Claude, ChatGPT, and Cursor.