AI user interview analysis
AI user interview analysis.
Explore themes. Check the evidence.
Use AI to summarize interviews, explore questions across selected transcripts, and identify useful moments. Keep your judgment at the center of qualitative research analysis.
14-day trial · No credit card required
Move from interview transcripts to research questions
Tetra combines interview recordings, transcripts, and AI assistance in your research project. Start with uploaded interviews or sessions captured by TetraBot, then use summaries and chat to explore what participants said.
Summarize a session
Get an overview of an interview, with summary sections that can be configured around the questions your study needs to answer.
Ask across interviews
Explore the selected transcripts in a project conversation. Ask about recurring problems, differences between experiences, or questions for a follow-up study.
Review useful moments
Use suggested snippets and tags as a starting point. Check the transcript and recording before treating a suggestion as a finding.
Start with a recap. Return to the interview.
A recap of Maya’s onboarding interview surfaces two connected topics: difficulty finding invitations and the workaround that helped her team proceed. Explore either theme, then inspect the source quote before building a broader interpretation.
Team invitations were difficult to discover during first-time setup.
Maya was asked to walk through the first time she invited her team.
“I kept looking on the project page.”
Maya · Operations lead · 04:12
Give qualitative analysis a consistent vocabulary
Tags help you compare related moments across interviews. Define what a tag means, use AI-assisted tagging to surface relevant excerpts, and review the matches against your research question.
For example, a tag called “invitation friction” is more useful when its definition explains what to include: difficulty finding, sending, or accepting a team invitation. Distinguish that from a broader setup problem so the results remain interpretable.
The goal is a set of clips and notes you can inspect, organize, and refine. AI suggestions are editable inputs to analysis; the researcher decides what supports the conclusion.
See how TetraBot tagging works“I couldn’t see how to invite the team. I kept looking on the project page.”
A specific match: difficulty locating the invitation control.
A practical interview-to-finding workflow
Imagine you are studying why new users struggle to invite teammates. The following is an illustrative research example, not a customer result or an AI-generated finding from real participants.
- Choose the evidence. Bring the relevant onboarding interviews into the project conversation.
- Ask a focused question. Try: “Where did participants get stuck inviting teammates? Identify supporting moments and any experiences that differ.”
- Review the suggested pattern. Revisit the source material. Check whether the problem was finding the invitation control, understanding permissions, or something else.
- Save and organize supporting snippets. Apply your tags and write down the interpretation, including exceptions and questions that remain unanswered.
- Share a reviewed finding. Create an insight with supporting clips, then export a highlight reel when stakeholders need to hear the relevant moments.
Make the interpretation yours
AI can miss context, misread an answer, or suggest a pattern that is not well supported. Check important findings against the original material, keep contradictory evidence visible, and avoid treating a few interviews as a measure of how common a problem is across all customers.
Once you have reviewed the analysis, keep the interviews and findings in your user research repository so your team can revisit them as new questions arise.
Read the AI analysis feature guideQuestions before you start
- Can I analyze several user interviews together?
- Yes. Use a project conversation with the relevant interviews to ask questions across those transcripts. Keep the selection aligned with your study so you know which material the answer draws on.
- Can I customize interview summaries?
- Yes. Summary sections can be configured with prompts that reflect the information you want to pull from a session.
- Does AI replace manual coding and synthesis?
- No. It helps with summaries, retrieval, and suggested tags or snippets. Researchers still review the evidence, refine the coding, and decide how to interpret and communicate findings.
- Is this software for job interviews?
- Tetra is designed for user and customer research interviews: understanding needs, experiences, and product feedback. This workflow focuses on qualitative research rather than candidate assessment.
Try it with your next research project.
Start with existing interviews or recruit participants for a new study. Choose the workflow that fits your team.
Participant recruitment is priced separately. See pricing for plan details.