Run comparative
thematic analysis side by side.

A different approach to thematic analysis.

Thematic analysis is the process of identifying, coding, and interpreting patterns across qualitative data: interviews, focus groups, open-ended survey responses, and field notes. There is not one way to do it. Coding-based analysis sorts data into categories first. Narrative analysis follows the story a participant tells. Grounded theory builds themes up from the transcripts themselves. Comparative analysis sets one group or time period against another. Concept mapping lays the relationships between themes out visually. Choosing the right one, and running it consistently, is where most projects lose time.

Why coding by hand breaks down

For most research teams, the practice never matched the method. A codebook gets built in a spreadsheet, applied to the first ten transcripts carefully, then applied a little differently by whoever is coding transcript forty. Comparing themes across two cohorts means two people's coding habits, not one consistent read. By the time a project needs a comparative thematic analysis between a pre- and post-intervention group, the codes from each side were never applied the same way to begin with.

Reading the data, not just the words

Speak AI treats every transcript the way a careful second coder would, at machine speed. Each interview or focus group is transcribed in your language, with 100+ supported, and then the recording itself is read on three layers: the words spoken, the tone and energy in the delivery, and where relevant, the visuals in a video session. Codes are applied against your codebook, or left to emerge inductively, and every theme keeps the quote and timestamp it came from.

Then the questions start. Ask across your entire dataset with AI chat, using Claude, ChatGPT, or Gemini depending on the task, the same way a research assistant would work through a codebook, except it runs the same way on transcript one and transcript four hundred.

What researchers ask their datasets

• “Compare the themes in the pre-intervention interviews against the post-intervention ones.”
• “Which codes show up more often in the group that dropped out?”
• “Pull every quote coded as ‘disengagement’ across all forty interviews.”
• “Does this new transcript confirm or break the pattern we saw in the last ten?”
• “Map how these five themes relate to each other across the whole dataset.”

From a coding pass to a comparison you can defend

The result is a codebook applied the same way on transcript one and transcript four hundred, whichever type of thematic analysis the project calls for. Comparisons between cohorts, sites, or time periods run on the same coded data instead of two people's separate reads, and dashboards you can customize and white-label track theme frequency and consistency over the life of a study, so this wave is measured against the last one on the same terms. One legal intelligence firm ran this kind of comparative analysis at scale, using large-scale comparative analysis across 5,100+ hours of carrier calls and saving $700K without adding headcount to the review team.

And because coding rarely stays inside one method, the same engine carries a codebook from a grounded-theory pass into a nitel kodlama workspace built for the next comparative wave, with the full history queryable through the MCP sunucusu.