Turn transcripts
into coded themes.

A different approach to coding in qualitative research.

Coding in qualitative research is the process of breaking interview and focus group data into labeled segments, then grouping those labels into categories that explain what is actually happening in the data. Open coding names what is there. Axial coding groups related codes together. Selective coding narrows in on the core categories that explain the data set, and theoretical coding tests an existing framework against what you found. Researchers have used all four for decades to move from a stack of transcripts to a defensible set of findings.

Why manual coding breaks down

The four stages hold up in theory. In practice, most teams code in a spreadsheet or a wall of sticky notes, applying the same code a little differently on a Friday afternoon than they did on a Monday morning. A codebook drifts across a team of research assistants. A 90-minute interview takes hours more to code by hand before the analysis even starts, and by the time twenty interviews are coded, the earliest ones need a second pass to match the later definitions.

Coding at every layer, not just the transcript

Speak AI applies your codebook the way a trained qualitative analyst would, at machine speed. Each interview or focus group is transcribed in your language, with 100+ supported, then coded across three layers: the words themselves, the tone, emotion, and energy in how they were said, and any visuals or screen shares captured alongside. Open codes are applied consistently across every transcript, related codes are grouped into axial categories, and coding can run across multiple models, including Claude, ChatGPT, and Gemini, depending on the task.

Then the questions start. Ask across your entire coded dataset with AI chat, using the same prompt workflows researchers once ran manually in NVivo or ATLAS.ti, now running natively over your transcripts.

What researchers ask their coded data

• “What are the most frequent open codes across this study, and which transcripts contain them?”
• “Group these codes into categories the way axial coding would.”
• “Which participants mentioned trust or hesitation, and what did they actually say?”
• “Does this data support or contradict our existing framework?”
• “Show me how this code’s frequency changed across our last three studies.”

From a stack of transcripts to a defensible codebook

The result is a codebook applied the same way on transcript one and transcript two hundred, with every code traceable back to the exact quote it came from. Categories that once lived in a colleague’s head become dashboards you can customize and white-label, tracking code frequency and theme trends over time, so this quarter’s interviews are measured against last quarter’s. A global market research firm put its qualitative studies through this workflow and saved $60K and 950+ hours, without adding headcount.

And because coding rarely lives alone, the same engine scores calls and interviews on the same criteria, connecting your coded transcripts to call scoring and the broader MCP layer other teams already use.