SUMMARIZATION

Sales-Objection Tracker from Calls to Airtable

On a daily schedule, scans new call transcripts for sales objections, categorizes each by type and deal stage, and logs them as rows in an Airtable objection register.

CategorySummarization
Enginesim
Difficultyintermediate
Triggerschedule
Steps6
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerDaily schedule fires each evening
  • ActionList today's new transcriptsGoogle DriveGoogle Drive
  • ActionRead each transcriptGoogle DriveGoogle Drive
  • ActionExtract and classify objectionsOpenAI
  • LogicDrop low-confidence extractions
  • OutputAppend rows to Airtable objection registerAirtableAirtable

What it does

Builds a living register of the objections prospects raise on sales calls. Each day it reads new transcripts, isolates objection moments, classifies them by category (price, security, timing, competitor, integration gap) and where in the funnel they occurred, then appends structured rows to Airtable for the sales team to analyze and rebut.

When to use it

Use it when sales leadership wants hard data on what's blocking deals instead of anecdotes. Ideal for refining battlecards, prioritizing roadmap gaps that cost revenue, and spotting a new competitor objection before it spreads.

How it works

  1. 1A daily schedule triggers each evening.
  2. 2Google Drive lists transcripts added that day.
  3. 3Each transcript is read in turn.
  4. 4OpenAI extracts every objection with its category, deal stage, severity, and the rep's response.
  5. 5A logic step filters out low-confidence extractions below a threshold.
  6. 6Each remaining objection is written as a new row to the Airtable objection register.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect Google DriveDocs, sheets, slides, files.
  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.

Run this workflow in your colony.

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