CONTENT CREATION

Airtable content calendar to batch localized clip factory

Runs daily over an Airtable content calendar, picks rows queued for video, generates narration scripts in each row's locale set, renders an explainer clip per language.

CategoryContent Creation
Enginesim
Difficultyadvanced
Triggerschedule
Steps6
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerDaily schedule
  • ActionQuery Airtable rows where Video Status is QueuedAirtableAirtable
  • LogicExpand each row's Locales into per-language scripts
  • ActionNarrate each language variantElevenLabsElevenLabs
  • ActionRender per-language clips and save to DropboxReplicateReplicate
  • OutputWrite clip links and Done/Failed status to AirtableAirtableAirtable

What it does

Treats an Airtable content calendar as a production queue and manufactures localized explainer clips in batches, tracking status row by row.

When to use it

Use it when a content ops team manages publishing in Airtable and needs a reliable, auditable pipeline that fans each scheduled article out into per-language videos.

How it works

  1. 1A daily schedule triggers the run.
  2. 2Airtable is queried for rows where Video Status equals Queued.
  3. 3For each queued row, the linked article text is summarized into a base script.
  4. 4A branch expands the row's Locales field into one translated script per language.
  5. 5ElevenLabs narrates each language variant.
  6. 6Replicate renders a clip per language; assets are saved to Dropbox.
  7. 7Each row is updated with the clip links and its status set to Done or Failed.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect AirtableBases, tables, views, automations.
  2. 2
    Connect OpenAIModels, embeddings, files.
  3. 3
    Connect ElevenLabsText-to-speech, voice cloning.
  4. 4
    Connect ReplicateImage, video, and model inference.
  5. 5
    Connect DropboxFiles and folders.
  6. 6
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  7. 7
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  8. 8
    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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