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Generative AI

Translation moves words; localization moves assumptions

A translated page reads correctly but lands wrong. The pricing example uses a currency nobody there uses. The reference is to a company nobody there knows. The directness that seems confident in one market seems rude in another.

None of that is a translation error. It is a set of assumptions that traveled with the text and should not have done so.

Doing this by hand for six markets is six times the work of writing the original, which is why most teams translate and hope. Doing it as one generation per market, each with its own rules about currency, date format, formality, and forbidden phrasing, costs one setup.

The reviewer is the constraint, not the generation. The output has to tell the reviewer where to look: every deliberate departure from the source, listed, so a native speaker checks decisions rather than rereading a page.

How the ActionFlow runs on the canvas

Two inputs start the flow: the source content and the list of target markets. A Google Sheets node holds the per-market rule set including currency, date format, formality level, phrasing to avoid, and any regulated claim that cannot cross that border. This keeps the rules somewhere a marketer can edit without opening the flow.

A Loop node processes the markets. Inside it, a Generate Object node returns two items per market: the adapted draft and an array of changes from the source with a reason for each.

Returning the change list as a structure instead of prose speeds up review. The reviewer reads eight decisions instead of eight hundred words.

A Notion node writes one page per market so drafts do not overwrite each other and a native reviewer can comment in place. A Slack node from the Human in the Loop category notifies the reviewer assigned to that market.

An If node checks the rule set for a "do not localise" flag before generating. Content that should not cross a border produces a notification instead of a draft.

Model choice per market is worth setting deliberately. Providers differ substantially in language coverage. This is one of the few flows where the right answer is not the same model everywhere.

Nodes this ActionFlow uses

  • Start: receives the source content and the market list.
  • Google Sheets (Integration): holds the per-market rule set where a marketer can edit it. Airtable fits the same slot.
  • If (Control): stops before generation when a market is flagged as do-not-localise.
  • Loop (Control): walks the markets one at a time.
  • Generate Object (AI Core): returns the adapted draft plus a structured list of every change and its reason.
  • Notion (Integration): writes one page per market for in-place review.
  • Slack (Human in the Loop): routes each draft to the reviewer who speaks that language.

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