Turn Customer Interviews Into Decisions Your Team Can Use
Turn interview notes into a reviewable decision log without treating a model summary as customer research.
Start with evidence, not a request for themes
A pile of call notes is not a research finding. Give each interview a stable ID, the customer segment, the question asked, the exact answer or timestamp, and any limitation. Remove names and details your team does not need before sending a copy to an AI workflow.
Ask the workflow to return a claim, the supporting interview IDs, a short quote, and a confidence note. A claim without a source stays a hypothesis. That single rule keeps a tidy summary from becoming invented certainty.
Run one small, reviewable synthesis
Serverless Inference fits a small server-side request when interviews arrive unevenly and you do not want to operate an inference endpoint. Put the model access key in your secret manager, send only the approved extracts, and retain the input and response with the study record.
Begin with five interviews from one segment. Compare every proposed theme against the original notes before you add it to a roadmap or a sales deck.
Return JSON only. For each finding include: finding, evidence_ids, supporting_quotes, counterevidence_ids, confidence, and follow_up_question.
Do not infer frequency from fewer than three interviews. Say "insufficient evidence" when the notes do not support a finding.Verify this workflow before scaling it
- Input
- Prepare five interviews from one segment with a stable ID, the question asked, a verbatim answer, and a redaction review.
- Build
- Request findings with supporting IDs, counter-evidence, quoted language, and a follow-up question.
- Expected result
- Each proposed pattern links back to source interviews and separates observation from the roadmap decision.
- Stop if
- Stop if a finding has fewer than three supporting interviews or has no traceable quote.
- Next step
- Review the evidence with the product owner and record the decision separately from the synthesis.
Separate a pattern from a decision
The model can group language. It cannot decide whether a request fits strategy, contract obligations, or the cost of building it. Keep a decision log with the finding, owner, decision, date, and reason. That makes later reversals understandable instead of embarrassing.
Review disconfirming evidence on purpose. If one customer reports the opposite experience, show it beside the dominant pattern rather than burying it in a summary.
Know when this is the wrong tool
Do not use a quick synthesis for regulated interviews, raw call recordings, or a high-stakes customer commitment unless your privacy and legal review permits that data flow. A local or approved research system may be the right answer.
The first successful test is modest: a product manager can trace each proposed finding back to an interview in under a minute. If they cannot, improve the evidence format before increasing volume.