translation-workflow

What problem does it solve?

It eliminates ad-hoc, inconsistent, and expensive translation processes by providing a structured workflow for localization from source extraction through QA, integration, and translation memory updates.

Core Features & Use Cases

• End-to-end translation workflow: extraction, TM analysis (including fuzzy matching and cost estimation), optional MT with post-editing, human translation coordination, QA, integration back into deliverables, and testing.
• Translation memory (TM) management: benefits-driven consistency, format support (TMX/XLIFF and common TMS formats), and operational practices like updates, de-duplication, and archiving.
• Machine translation (MT) + post-editing strategy: API and self-hosted options, with clear guidance for light vs full post-editing and QA-oriented review.
• Quality assurance and metrics: QA checklists covering accuracy/consistency/completeness/localization, plus practical translation and cost metrics (including TM match tiers and MT post-editing effort).

Quick Start

Ask your AI to design a translation workflow for your project that specifies TM matching, optional MT with post-editing level, QA checklist, and the steps to update TM and glossaries after integration.