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.
How it runs
The automated pipeline, trigger to output.
- TriggerOperator starts run with target locale list
- ActionFetch master landing frame + text layers from FigmaFigma
- ActionGenerate localized copy variant per locale with OpenAIOpenAI
- LogicBrand QA gate: banned terms, claim limits, length, disclaimers
- OutputPublish approved variants to R2 as copy bundlesCloudflare 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
- 1A manual run kicks off with a list of target locales.
- 2The master landing frame and text layers are fetched from Figma.
- 3OpenAI rewrites each text layer per locale, preserving tone, length budget, and CTA intent.
- 4A QA logic gate checks each variant against brand rules (banned terms, claim limits, required disclaimers, length).
- 5Approved variants are written to R2 as structured copy bundles.
- 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.
- 1Connect FigmaFiles, frames, comments, assets.
- 2Connect OpenAIModels, embeddings, files.
- 3Connect Cloudflare R2Object storage, S3-compatible.
- 4Connect SlackChannels, DMs, threads, mentions.
- 5Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 6Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 7Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
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