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In practice
The failure of machine translation is not vocabulary; it is register: the translated text carries the source language’s cadence, its idioms arrive literal, its emphasis lands in the wrong syllable — and every native reader, starting with the editor you’re pitching, recognises it within a sentence. For placements the cost is precise: pitches that read as translated templates are deleted as bulk mail; articles that read as translated content are rejected or published with a shrug on the sites that accept anything; and anchors that read as translations mark the campaign as foreign in the exact market it was trying to enter.
The native editor’s job runs deeper than polishing: choosing the market’s actual terminology (the category may be called something the source language never anticipated), adjusting claims to the market’s expectations (what a German dev audience accepts as evidence differs from a US one), shaping the pitch to the publication’s conventions, and writing the descriptive anchors from the market’s own phrasing.
The process that respects the economics: the AI drafts in the target language from the start (a brief, not a manuscript to translate), the native editor shapes — the expensive human time goes to judgement, not conversion — and the client’s approval pack carries the English summary so the approval is informed. The verification of the whole layer: ask the market — a placement’s quality is legible to its audience in seconds, which is exactly how long the audience gives it.
See also: Localisation of content for link markets, Multilingual anchors, International link building.
Related service: International links.
