About thematicanalysis.ai

What the tool actually does

Qualitative research produces two kinds of data problems, and this tool is built around both of them.

Both start the same way. You paste in your source text (or upload PDFs, Word documents, or spreadsheets) and choose a framework. The engine generates an initial codebook, then clusters related codes into candidate themes, following the phase structure of whichever method you picked. Every code carries the exact quote it came from, and every theme lists the sources and codes behind it, so you can check each claim against your own data rather than trust a black box. From there, you export a Word or Markdown report with your themes, evidence tables, and a full audit trail, ready to work into your methods and results chapters.

Why Braun and Clarke's framework sits at the centre of this

Braun and Clarke's (2006) six-phase method is the most widely cited and widely taught approach to thematic analysis in the social sciences, and it is the default framework here for exactly that reason: if you have been taught thematic analysis in a research methods course, this is very likely the version you learned.

Their later work on reflexive thematic analysis (Braun and Clarke, 2019) is also where the tool's core design principle comes from: the idea that a theme is not a neutral pattern waiting to be extracted from data, but an interpretive claim the researcher builds and remains responsible for. That is why every AI-generated code and theme here is presented as a first pass for you to review, rename, and reason through, not a finished answer.

If you want the full background on the method itself, our guide on what thematic analysis is and how the six phases work walks through Braun and Clarke's process in detail, and our guide on the difference between a code and a theme covers the distinction that trips up most new researchers.

Braun and Clarke's method is not the only one supported. For literature reviews specifically, the tool also offers Thomas and Harden's (2008) thematic synthesis approach, built for exactly the task of synthesising findings across multiple qualitative studies, alongside Framework Analysis, Grounded Theory, and Qualitative Content Analysis, so the framework you choose matches the method you were taught rather than the other way around.

Built for literature reviews as much as primary research

A literature review chapter and a set of interview transcripts pose different analytical problems, so the tool provides a dedicated mode for each.

Both modes support between 3 and 20 sources per analysis, are available on every plan, and produce the same kind of evidence-linked output either way.

Why this exists

Generic AI chatbots can summarise a transcript in seconds, but a conversational summary is not a defensible piece of research. It has no fixed methodology you can name in your methods chapter, no reliable link back to the source text, and a well-documented tendency to blur or invent details when asked to work across several sources at once. That gap, between what a chatbot produces and what a dissertation committee or peer reviewer actually needs to see, is what this tool is built to close: a structured process built around a named, citable framework, with every code checked against the source text it came from.

It is also built to be considerably more accessible than the CAQDAS software most qualitative researchers are pointed toward by default. A student licence for legacy tools often runs into the hundreds of dollars once AI features are included, on top of a steep learning curve. Here, your first three sources are free with no signup or card required, and a full analysis of up to twenty sources is a single flat payment, with results in your browser in minutes. If your project outgrows this tool, or needs long-term team-based codebook management, your work exports directly into NVivo, ATLAS.ti, MAXQDA, or Dedoose through a standard REFI-QDA file.

What this tool is not

It is not a replacement for your own interpretive judgement, and it does not claim to be. Braun and Clarke (2019) are explicit that theme development is a reflexive, researcher-led process, and every report generated here states this plainly: review each AI-suggested code against its quote, rename and refine themes to fit your actual research question, and disclose AI assistance in your methods section where your institution or journal expects it.

Our guide on how to report AI-assisted thematic analysis in your methods section covers exactly how to do that in a way that satisfies APA, Harvard, and other common referencing standards. The tool makes the mechanical first pass through your data fast and traceable; the analysis itself is still yours.

Who it is for

• Doctoral candidates and master's students synthesising a literature review chapter or coding primary data for a dissertation.
• Faculty and research teams who need a fast, transparent first pass through interview or survey data before deeper manual analysis.
• University lecturers introducing students to thematic analysis methodology and evidence tracing.
• Applied qualitative practitioners working on policy evaluation, program review, or healthcare research where claims must hold up to scrutiny.

Frequently asked questions