AI Translation / Localization Pipeline

How It Works

Tell it your content, languages and qa. This generates a localization pipeline the way an experienced automation strategist would build it - a real, usable deliverable, not a generic checklist. The output follows the standard: localization pipeline doc with workflow stages, TMS/glossary, QA/LQA model, and integration steps. Replace every [[token]] with your specifics and it is ready to implement.

What to Provide

AI Translation / Localization Pipeline

This is the finished deliverable.

1. Source/target locales and content types

Write the specific rule for your situation here: [[the concrete policy, threshold, or owner for source/target locales and content types]]. Base it on your content and adjust as real cases come in.

Signal of expertise this section should show: TEP and MTPE distinction.

Mistake this guards against: Raw MT with no review/QA.

2. TM and termbase/glossary management

Define this concretely: [[translation memory]] - spell out the actual rule, not just that one exists.

Signal of expertise this section should show: MQM/DQF quality scoring.

Mistake this guards against: No glossary/TM.

3. Workflow stages

Lay this out as a stage-by-stage pipeline: [[extract]] → [[MT/translate]] → [[TEP edit/proof]] → [[QA]] → [[in-context review]] → [[publish]]. For each stage, write down who or what system owns it and what "done" looks like before the item moves to the next stage.

Signal of expertise this section should show: ICU MessageFormat/plural and placeholder integrity.

Mistake this guards against: Ignoring placeholder and plural handling.

4. MT-vs-human and post-editing policy

Define this concretely: [[MTPE]] - spell out the actual rule, not just that one exists.

Signal of expertise this section should show: locale-specific formatting (dates, currency, RTL).

Mistake this guards against: Treating localization as just translation (no locale formatting/cultural adaptation).

5. QA checks

Cover each of these explicitly rather than leaving them implied: [[terminology]], [[placeholders]], [[length]], [[locale formats]]. Define [[the specific rule or default for terminology]] so nothing is left to guesswork.

Signal of expertise this section should show: TEP and MTPE distinction.

Mistake this guards against: Raw MT with no review/QA.

6. File/string handling

Cover each of these explicitly rather than leaving them implied: [[i18n keys]], [[ICU plurals]]. Define [[the specific rule or default for i18n keys]] so nothing is left to guesswork.

Signal of expertise this section should show: MQM/DQF quality scoring.

Mistake this guards against: No glossary/TM.

7. LQA scoring

Cover each of these explicitly rather than leaving them implied: [[e.g. MQM]], [[DQF]]. Define [[the specific rule or default for e.g. MQM]] so nothing is left to guesswork.

Signal of expertise this section should show: ICU MessageFormat/plural and placeholder integrity.

Mistake this guards against: Ignoring placeholder and plural handling.

8. CI integration and update/re-sync flow

Write the specific rule for your situation here: [[the concrete policy, threshold, or owner for ci integration and update/re-sync flow]]. Base it on your content and adjust as real cases come in.

Signal of expertise this section should show: locale-specific formatting (dates, currency, RTL).

Mistake this guards against: Treating localization as just translation (no locale formatting/cultural adaptation).

Worked Examples

Example 1 - Product docs → 6 languages

Inputs: Help center + product UI strings, ES/DE/JA/PT-BR/FR/KO, in-house + LQA vendor

Result: MTPE workflow cut turnaround from 3 weeks to 4 days per language while meeting MQM quality bar.

Example 2 - Mobile app UI strings

Inputs: ICU plural strings, RTL for Arabic launch

Result: Placeholder/plural QA caught 40+ broken interpolations before release that manual review had missed.

Format Checklist

Common Mistakes to Avoid

• Raw MT with no review/QA.
• No glossary/TM.
• Ignoring placeholder and plural handling.
• Treating localization as just translation (no locale formatting/cultural adaptation).

Next Steps After You Generate This

Week 1: pilot with a small internal group or a single channel/segment. Week 2: review real output against the format checklist below and fix the top 2-3 gaps. Weeks 3-4: expand scope and set a recurring review cadence so the workflow stays accurate as your data and process change.

This deliverable gives you a working starting point on day one - keep the [[tokens]] current as your process, tools, and volume change.