AI-assisted qualitative analysis
From interviews to outputs you can trace back.
Qualitative Analysis Workflow helps researchers turn interview transcripts into analytical artifacts: from evidence and codes through themes and matrices to clear, defensible findings. AI speeds up the first pass of analysis; interpretation and final responsibility stay with the researcher.
AI speeds up the first pass of analysis. It does not replace judgment.
When an analysis stays in a chain of linked artifacts, you can trace back from a finding to a theme, a code, and the specific evidence. That gives the work structure and room for expert review.
Start with your own data
Add a transcript and a research goal. Optionally include an outline, field notes, contextual metadata, terms, a framework, a codebook, or an evaluation framework.
Keep the analytical trail
Each step builds on the last: evidence and citations, codes and codebook boundaries, themes, matrices, and analytical outputs.
Check and revise
An AI critic reads the key outputs by the rules of the method, and a serious point leads to one targeted revision. The critic is the same model that wrote them, so this is not independent or human validation; every output still needs the researcher's judgment.
How it works
Three clear steps
Add your materials
A transcript and a goal are the foundation. Add more context only when it helps your specific task.
Choose a starting preset
Pick the analytical approach that matches what you want to learn from the data.
Read, verify, revise
Work through the linked artifacts, compare their revisions, and use them as the basis for your own analysis.
Starting paths
Start based on the nature of your research.
Presets give the analysis a clear starting shape without turning it into a one-size-fits-all template.
Template analysis for applied briefs
A practical path from transcript to evidence-backed decisions.
AI-assisted inductive thematic analysis
For discovering patterns and themes that emerge from the data.
Deductive qualitative content analysis
For working with a prepared codebook, terms, or framework.
Framework Method evaluation
For structured evaluation against an evaluation framework.
Rapid qualitative analysis
For a quick overview by topic across groups, without coding every passage.
Starting presets: Template analysis for applied briefs, AI-assisted inductive thematic analysis, Deductive qualitative content analysis, Framework Method evaluation and Rapid qualitative analysis. An AI critic, the same model that wrote the analysis, reads its key outputs; this is not independent validation.
Outputs and integrity
Analysis should be readable in reverse, too.
Qualitative Analysis produces inspectable artifacts with evidence, citations, and provenance: Markdown throughout, with Word and spreadsheet exports for selected outputs. That lets you trace where a code or theme came from and where it needs your own correction.
- Evidence and citations as a basis for interpretation
- Codes, codebook boundaries, themes, and matrices
- Analytical outputs you can read and revise
Pricing
Pay as you go. No subscription.
Buy credits when you need them. One credit is worth 0.01 USD or 0.01 EUR.
Starter
2,000 credits for 20 USD or 20 EUR.
Growth
6,000 credits for 60 USD or 60 EUR.
What you pay for
Each operation — a workflow step, a cross-check, or a deliverable — is charged by model usage, with a minimum of 250 credits (2.5 USD). You see an estimate before you run it; actual usage is settled after completion.
Frequently asked questions
What do I need to get started?
A transcript and a research goal are the essentials. Everything else is optional and helps ground the analysis further.
Does Qualitative Analysis replace the researcher's work?
No. It helps create and structure analytical material; interpretation, review, and final responsibility remain with the researcher.
How much does an analysis cost?
You pay only for what you run, with prepaid credits (2,000 credits for 20 USD/EUR or 6,000 credits for 60 USD/EUR). The cost depends on the length of your materials, the models and the steps you choose; the app shows an estimate before each run. If an operation fails or its output is unusable, you can file a credit claim for that step.
How are the outputs checked?
An AI critic reads the key outputs of the analysis by the rules of its method, and a serious point leads to one revision of the part concerned. The critic is the same model that wrote them, not a second model and not a human, so it can miss problems. Quotations are checked word for word against the transcripts by program code. It is an aid for checking, not a substitute for expert review.
Which languages can the analysis be written in?
The interface is available in 32 languages. We have verified the quality of the analysis itself (verbatim quotations, method, natural language) in English, Czech, German, Arabic, Japanese, French, Spanish, Korean, Brazilian Portuguese and Simplified Chinese. Other languages work, but their analysis quality has not been verified yet.
Ready to start?