Turn transcripts
into context you can code.

A different approach to context in discourse analysis.

Discourse analysis studies language in use: how people communicate, and how the meaning of what they say depends on where, when, and to whom they say it. Context is the part of that picture the words alone cannot carry. It is the setting, the participants, the social norms in the room, and everything said before a given turn that shapes how a listener actually understands it. Researchers, linguists, and qualitative teams rely on context to explain why the same sentence can mean two different things in two different rooms.

Why manual context-coding breaks down

Coding context by hand means re-listening to the same recording more than once: once for the words, once for who was in the room and what came before, once for the tone that changed what those words meant. A transcript on its own throws most of that away. Two coders working from a transcript alone, without the recording, regularly disagree on whether a line was sarcastic or sincere, deferential or resistant, because the words don’t carry the register, the pacing, or the relationship between speakers that gave the line its meaning in the first place.

Reading context, not just words

Speak AI transcribes what was said in your language, with 100+ supported, then reads the recording itself the way a discourse analyst reads a room: the tone, pace, and energy of each speaker, and for video, the visual cues that sit alongside the talk. That three-layer read, the words, how they were said, and what was visible, gets coded into structured fields your framework can use: setting, participants, register, power dynamics, and the co-text immediately before and after each turn. Coding runs on whichever model fits your codebook, Claude, ChatGPT, or Gemini, so the categories match your framework rather than a generic sentiment score.

The four types of context, coded consistently

• Situational context: the setting, the people present, and what prompted the exchange, flagged automatically for every transcript.
• Linguistic context (co-text): the sentences immediately before and after a turn, kept attached instead of stripped into an isolated quote.
• Cultural context: norms, idioms, and shared references specific to the speakers’ community, surfaced instead of assumed.
• Cognitive context: what each speaker already believed or expected going in, inferred from tone, hedging, and the history of the conversation.

From a single transcript to a trend over time

Coded that way, one transcript stops being an isolated data point. Ask across your entire interview or call history with AI chat, using ChatGPT, Claude, and Gemini built in natively, and dashboards you can customize and white-label track how register, sentiment, and context frames shift over time, so this quarter’s interviews are measured against last quarter’s instead of read cold. A legal intelligence firm put its case files through this workflow and processed 5,100+ hours of calls and saved $700K, coding context at a scale no team could manage by hand. And because a transcript rarely lives alone, the same engine works over your existing recordings from Claude or Cursor through the MCP server, so a coded corpus becomes something you can query, not just store.