CUSTOMER SUPPORT
Back-Translation QA Gate for Translated Support Macros
When a localized support macro is published, round-trips it through back-translation and flags any meaning drift before it goes live, posting a pass/fail verdict to Slack.
How it runs
The automated pipeline, trigger to output.
- TriggerNotion macro marked Ready for QANotion
- ActionBack-translate localized text to source languageOpenAI
- ActionScore semantic equivalence vs originalOpenAI
- LogicBranch on fidelity score vs threshold
- ActionUpdate macro status + attach diffs in NotionNotion
- OutputPost pass/fail verdict to SlackSlack
What it does
Guards your localized macro library so a mistranslated canned reply never reaches a customer. Whenever a translated macro is added or edited in Notion, it back-translates the localized text to the source language, compares meaning against the original, and blocks publish if the gap is too large.
When to use it
Run this when you maintain canned responses in multiple languages and need a consistent QA step before any localized macro is marked live. It catches the silent failures — softened apologies, dropped conditions, flipped negations — that human spot-checks miss.
How it works
- 1A macro row in Notion flips to status "Ready for QA", triggering the workflow.
- 2OpenAI back-translates the localized macro into the original source language.
- 3OpenAI scores semantic equivalence between the original and the back-translation, returning a 0-100 fidelity score plus a list of meaning changes.
- 4A logic branch checks the score against your threshold (e.g. 90).
- 5On pass, the macro status is set to "Published" in Notion; on fail it is set to "Needs rework" with the flagged diffs attached.
- 6A Slack message reports the verdict, score, and any flagged phrases to the localization channel.
Set it up
What you configure once, before turning it on.
- 1Connect NotionPages, databases, comments.
- 2Connect OpenAIModels, embeddings, files.
- 3Connect SlackChannels, DMs, threads, mentions.
- 4Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 5Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 6Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
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