SUMMARIZATION

Classify Zoom Call Objections with a Hugging Face Model and Log to Coda

Runs each Zoom sales-call transcript through a Hugging Face zero-shot classifier to label objections into a fixed taxonomy, then logs the labeled.

CategorySummarization
Enginesim
Difficultyadvanced
Triggerevent
Steps5
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerZoom sales call recording completedZoomZoom
  • ActionSegment transcript into objection passagesZoomZoom
  • ActionZero-shot classify each passage with Hugging FaceHugging FaceHugging Face
  • LogicKeep passages above the confidence threshold
  • OutputAppend labeled verbatim objections to Coda tableCodaCoda

What it does

Gives you a consistent, model-backed objection taxonomy instead of free-form summaries. Each Zoom transcript is segmented into objection moments, and a Hugging Face zero-shot classification model assigns every moment to one of your defined labels (budget, timing, integration risk, security, decision authority). The verbatim text, label, and confidence score land in Coda as structured rows.

When to use it

Use when you want analyzable, queryable objection data with stable category labels you can pivot on, and you prefer a dedicated classification model over prompting an LLM for categories. Good for RevOps teams building dashboards on objection frequency.

How it works

  1. 1A Zoom recording-completed event delivers the finished call.
  2. 2The transcript is segmented into candidate objection passages with timestamps.
  3. 3Each passage is sent to a Hugging Face zero-shot classifier against your label set.
  4. 4A logic step keeps only passages above the confidence floor and attaches the winning label.
  5. 5Each labeled verbatim objection is appended as a row to the Coda objection-tracking table.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect ZoomMeetings, recordings, transcripts.
  2. 2
    Connect Hugging FaceModels, datasets, spaces — the open-source hub.
  3. 3
    Connect CodaDocs, packs, 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.

14-day trial. No DevOps. No Sales call. Provisioned in under a minute.