Turn transcripts
till coded, searchable data.

A different approach to transcript coding.

Coding a transcript means tagging what was said with categories from a scheme: themes, sentiments, a priori codes, or codes that emerge from the data itself. It turns a wall of interview text into a coded transcript researchers can count, compare, and defend by quote, and it is the backbone of thematic analysis, grounded theory, and any qualitative study that needs to hold up under review.

Why manual coding breaks down

For most research teams, coding is where the timeline slips. A single hour-long interview can take three or four hours to code well by hand: reading the transcript twice, highlighting passages, deciding which code applies, checking it against the codebook, then doing it all again for the next interview. Multiply that across forty interviews and a coding pass becomes the bottleneck of the whole study, and inter-coder agreement drifts the longer the project runs.

Reading the transcript, not just the words

Speak AI codes every transcript the way a trained research assistant would, at machine speed. Each interview, focus group, or session is transcribed in your language, with 100+ supported, then read against your codebook: your a priori codes applied consistently, new codes surfaced where the data calls for them, and every code tied back to the exact quote that earned it. Because Speak AI also reads the recording itself, not just the transcript, codes for tone, hesitation, and emphasis sit alongside codes for what was literally said, so a flat “yes” and a reluctant “yes, I guess” are coded differently.

Then the questions start. Ask across your entire coded transcript set with AI chat, using the same coding logic your team built by hand, now running natively over your recordings with ChatGPT, Claude, and Gemini built in.

What teams ask their coded transcripts

• “Which interviews mention pricing as a switching trigger, and what did participants say?”
• “How often does each code appear across the full transcript set, by participant group?”
• “Show me every quote coded under ‘trust in the process.’”
• “Where do two coders disagree, and why?”
• “Summarize the codes that came up most in this quarter’s interviews.”

From a coded transcript to a defensible analysis

The result is a coded transcript your team can actually query instead of a spreadsheet nobody opens again. Code frequency and co-occurrence become a chart instead of a manual tally, and dashboards you can customize and white-label track how themes shift across waves of interviews, so this quarter’s codes are measured against last quarter’s. Olson Zaltman, a global market research firm, put its qualitative studies through this workflow and saved $60K and 950+ hours, without adding headcount.

And because interviews rarely live alone, the same engine analyzes calls, meetings, and recordings on the same coding framework, queryable straight from Claude, ChatGPT, and Cursor through the MCP server.