MARKETING

Localized landing-page variant generator with brand QA gate

Pulls a Figma master landing frame, generates localized copy variants per target market with OpenAI, runs them through a brand QA gate.

CategoryMarketing
Enginesim
Difficultyintermediate
Triggermanual
Steps6
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerOperator starts run with target locale list
  • ActionFetch master landing frame + text layers from FigmaFigmaFigma
  • ActionGenerate localized copy variant per locale with OpenAIOpenAI
  • LogicBrand QA gate: banned terms, claim limits, length, disclaimers
  • OutputPublish approved variants to R2 as copy bundlesCloudflareCloudflare R2
  • OutputPost rejected variants + failing rule to SlackSlack

What it does

Turns one approved landing-page design into market-specific variants. It reads the master frame from Figma, translates and culturally adapts the copy for each locale, then enforces a brand QA gate before anything ships. Clean variants land in R2 as ready-to-deploy assets; anything that trips the gate is routed to a human in Slack instead of going live.

When to use it

Use it when you launch one campaign across several regions and need consistent, on-brand copy without a translator-and-reviewer round trip for every locale. Ideal for growth and lifecycle teams running the same offer in multiple markets.

How it works

  1. 1A manual run kicks off with a list of target locales.
  2. 2The master landing frame and text layers are fetched from Figma.
  3. 3OpenAI rewrites each text layer per locale, preserving tone, length budget, and CTA intent.
  4. 4A QA logic gate checks each variant against brand rules (banned terms, claim limits, required disclaimers, length).
  5. 5Approved variants are written to R2 as structured copy bundles.
  6. 6Rejected variants are posted to Slack with the failing rule for an editor to fix.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect FigmaFiles, frames, comments, assets.
  2. 2
    Connect OpenAIModels, embeddings, files.
  3. 3
    Connect Cloudflare R2Object storage, S3-compatible.
  4. 4
    Connect SlackChannels, DMs, threads, mentions.
  5. 5
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  6. 6
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  7. 7
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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