MARKET RESEARCH
Zoom interview verbatim quote bank to Airtable
When a Zoom interview recording completes, this mines the transcript for high-signal customer verbatims, tags each by job-to-be-done and sentiment.
How it runs
The automated pipeline, trigger to output.
- TriggerZoom recording completedZoom
- ActionFetch interview transcriptZoom
- ActionExtract tagged verbatim quotesOpenAI
- LogicFilter out low-signal lines
- OutputAppend quotes to Airtable bankAirtable
What it does
Builds a reusable quote bank from interview transcripts. Instead of summarizing, it isolates individual high-signal customer verbatims, tags each one with the related job-to-be-done and a sentiment label, and appends every quote as its own row in Airtable so researchers and marketers can search and cite real customer language on demand.
When to use it
Use it when you need raw evidence at the sentence level for landing-page copy, pitch decks, or research readouts, and you want quotes filterable by job and sentiment rather than buried in long summaries.
How it works
- 1A Zoom recording-completed event fires for the interview.
- 2The flow fetches the recording transcript from Zoom.
- 3OpenAI extracts discrete verbatim quotes, each tagged with the related job-to-be-done and a sentiment label.
- 4A logic step filters out filler and off-topic lines so only high-signal quotes remain.
- 5Each surviving quote is appended as a row in the Airtable quote bank with its tags and a link to the source.
Set it up
What you configure once, before turning it on.
- 1Connect ZoomMeetings, recordings, transcripts.
- 2Connect OpenAIModels, embeddings, files.
- 3Connect AirtableBases, tables, views, automations.
- 4Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 5Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 6Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
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