AI Translation Overview
Introduction
AI translation in Smartcat combines your existing Translation Memory, best-of-breed machine engines, and built-in quality checks into a single, seamless workflow. The result: faster translations, consistent terminology, and lower review costs.
Why It Matters
Speed: Instantly translate new content by reusing past work (TM) and high-quality AI engines.
Consistency: Always apply your approved terminology from glossaries.
Cost-efficiency: Avoid paying for human review whenever an exact TM match exists and reduce post-edit time.
Workflow Diagram
Six-Step Overview
1. Document Segmentation
Smartcat first breaks each file into segments (usually one sentence apiece). Every core translation feature — TM lookup, AI translation, QA checks — operates at the segment level. Once AI translation completes, you or a reviewer will open the document in the CAT Editor, where you’ll see each segment ready for review.
2. Translation Memory Lookup
Each segment is checked against your Translation Memory (TM) for exact matches. When a reviewed translation exists, Smartcat applies it automatically — saving you time and effort in human review. By default, TM matches are confirmed automatically and do not require further review from human; matches will stay out of scope when you assign someone to review the document.
Placeholder: ADD YOUR SCREENSHOT HERE (TM match automatically confirmed in CAT Editor)
3. Machine Translation (AI Translation)
Segments without TM matches go through Smartcat’s AI translation routing, which selects the best available AI engine for that language pair. You can override this routing in two places:
Project Settings → Linguistic Assets → AI translation
AI Translation Profiles (details in a separate article)
4. Quality Checks
After translation, Smartcat runs automatic QA checks to catch issues such as:
Missing formatting tags
Incorrect numbers
Punctuation errors
Capital-letter mismatches
Target text is significantly shorter/longer than source text
etc.
One of the most important checks is for mistranslated glossary terms. If glossary errors are detected, Smartcat adds an extra step to correct them.
5. Glossary-Term Fix
When a QA check flags a glossary-term error (e.g., a preferred term wasn’t used), Smartcat triggers an OpenAI-based correction for non-LLM engines. LLM engines are trusted to handle glossary terms correctly, so this fix isn’t applied to them.
6. Fallback Translation
If a segment still has critical errors (such as missing formatting or a failed engine response) Smartcat automatically reruns translation using Google NMT as a reliable backup. In the future, you’ll be able to choose which fallback engine to use and enable or disable fallback via AI Translation Profiles.
With these six steps, Smartcat ensures your content is translated quickly, accurately, and consistently — leveraging both your past translations and the latest in AI technology. In the next articles, we’ll dive deeper into AI translation routing logic, profile settings, and glossary management.