back_office_ops · services · workflow
TransPerfect improves translation quality and efficiency using Amazon Bedrock for automated post-editing and transcreation
AWS and TransPerfect's localization workflow steps—including machine translation post-editing and transcreation—were often manual, costly, and time-consuming, with transcreation especially resistant to automation and dependent on highly skilled human linguists.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Translation memory lookup
The translation memory, a client-specific repository of previously translated and approved content, is applied first to maximize reuse of existing translations.
Tools used
Amazon BedrockGlobalLinkAmazon TranslateAmazon Bedrock GuardrailsAnthropic's ClaudeAmazon Nova Pro
Outcome
LLM-powered post-editing and transcreation delivered up to 50% cost savings for translations, up to 60% linguist productivity gains for transcreation, up to 40% cost savings across translation workflows, and up to 80% reduction in project turnaround times.
Results
Time savedup to 80%
Volumeover 95%
Cost replacedup to 50%
Grounding & classification
Source type: platform led case
37 fields verified against source quotes.
agent assistcontent generationdocument aitranslationknowledge basehuman review describedmetric backednamed customerproduction runtime claimedtools describedvendor confirmedworkflow describedprofessional servicessoftwareaccuracy improvementcost reductioncycle time reductionemployee productivitytime savedplatform led caseback office opsmarketing opsai draft human approvalautonomous resolutionhuman review queue