AI AGENTS

Zoom Demo Objection Trend Tracker in Airtable

After each Zoom demo, normalizes every objection into a typed category and logs one row per objection in Airtable.

CategoryAI Agents
Enginesim
Difficultybeginner
Triggerevent
Steps5
Setup~5 min

How it runs

The automated pipeline, trigger to output.

  • TriggerZoom recording completed for a demo callZoomZoom
  • ActionFetch transcript and host metadataZoomZoom
  • ActionExtract and normalize objections to taxonomyOpenAI
  • LogicDedupe objections and attach call context
  • OutputWrite one row per objection to AirtableAirtableAirtable

What it does

Builds a structured, analyzable record of objections over time. Every objection from every demo becomes a normalized Airtable row tagged with category, rep, deal size band, and outcome signal, so enablement can spot patterns like a competitor objection spiking after a rival's launch.

When to use it

Use it when you want data, not anecdotes. If you need to answer questions like "which objection costs us the most late-stage deals" or "which reps consistently beat pricing pushback," this turns scattered call audio into a tidy table you can pivot and chart.

How it works

  1. 1Zoom fires its recording-completed event for a demo.
  2. 2The flow fetches the transcript and host metadata.
  3. 3An OpenAI step extracts each objection and normalizes it to a fixed taxonomy (price, timing, competitor, authority, integration, trust).
  4. 4A logic step deduplicates near-identical objections within the same call and attaches the rep and call context.
  5. 5The workflow writes one Airtable row per normalized objection with category, rep, severity, and the rep's response.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect ZoomMeetings, recordings, transcripts.
  2. 2
    Connect OpenAIModels, embeddings, files.
  3. 3
    Connect AirtableBases, tables, views, automations.
  4. 4
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  5. 5
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  6. 6
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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