Find patterns across customer interviews
Pulls themes, quotes and surprises out of interview transcripts, with every claim traced to who said it.
Analyse these customer interview transcripts to answer: {{RESEARCH_QUESTION}}
<transcripts>
{{TRANSCRIPTS}}
</transcripts>
Report:
1. Themes: for each, how many interviews mention it, a one-line summary, and 2 direct quotes with the participant label.
2. Disagreements: where participants differ, and any pattern in who says what.
3. Surprises: things that contradict common assumptions.
4. Gaps: what we still do not know and should ask next time.
Rules: use only what is in the transcripts. Quotes must be exact. Do not count a theme if it is only implied.Example output
Theme: switching is triggered by a bad audit (4 of 6 interviews). "We only looked for new software after the auditor flagged it." (P3, finance lead) Surprise: nobody mentioned price as a reason to switch.
How to use
Label each transcript (P1, P2...) with role and company size. If they do not fit in one message, analyse in batches and then ask the model to merge the batch summaries.
Why it works
Requiring counts and exact quotes keeps the model grounded in evidence, which is where research synthesis most often goes wrong. Long transcripts can exceed the context window, so batching keeps every interview in view.