How it works
Message arrives
Support message received in any supported language
Detect language
DeepL identifies the source language automatically
Translate to English
Message translated for your support team to read
Send response
Agent's English reply auto-translated back to customer's language
Build with NIRA
Copy this prompt to create the workflow in seconds
When a support message arrives in @support_channel:
1. Detect the language of the incoming message using DeepL
2. If not English:
a. Translate the message to English using DeepL
b. Post to @internal_channel with format:
"[Language flag] [Language] message from [customer]:
Original: [original message]
Translation: [English translation]"
3. When support agent replies with "/respond [message]":
a. Translate the English response back to the detected language
b. Post the translated response to the original thread
c. Include both versions for transparency
4. Log language stats for reporting
Supported languages: All 29 DeepL languagesSupported languages
DeepL supports 29 languages with exceptional quality:
+ 17 more including Swedish, Danish, Finnish, Czech, Greek, and others.
Variations
Email support translation
Extend to email-based support channels.
When email arrives at support@company.com: Detect language, translate, create ticket with both versions, translate agent reply back. Auto-response for common questions
Combine with FAQ matching for instant answers.
After translating: Match against FAQ. If confidence above 90%, draft auto-response, translate back, send for agent approval. Language analytics
Track support volume by language for hiring decisions.
Log each translation: timestamp, language, word count. Weekly report: language distribution, top languages by volume. FAQ
Why DeepL instead of Google Translate?
DeepL consistently outperforms Google Translate for European languages in blind tests. The translations sound more natural and preserve context better, which matters for customer communication.
What about languages DeepL doesn't support?
For unsupported languages, the workflow can fall back to Google Translate or flag for manual handling. This covers edge cases while using DeepL for primary languages.
How do I handle technical terms?
DeepL supports custom glossaries. Add your product terms, feature names, and technical vocabulary to ensure consistent translation across all messages.
Can customers see both versions?
You can configure this either way. Some teams show "Translated from English" for transparency; others send only the translated version for a native experience.