AI detects the customer's language and drafts culturally appropriate responses. Bilingual reviewers verify accuracy before delivery.
Hiring multilingual support staff is expensive and hard to scale. Customers writing in non-English languages either wait longer for a bilingual agent or receive awkward machine translations that damage the brand. As businesses expand globally, the gap between language demand and support capacity widens.
MultiMail's AI agent detects the language of inbound support emails and drafts responses in the customer's preferred language with proper cultural context and locale-specific formatting. With gated_send oversight, bilingual reviewers verify translation accuracy and cultural appropriateness before delivery.
The AI reads incoming support emails and identifies the language. It also detects cultural context clues like formality expectations, date formats, and greeting conventions.
If your support knowledge base is in English, the AI translates the customer's question into English for internal processing, then translates the response back to the customer's language.
The agent drafts a culturally appropriate response in the customer's language using reply_email. The response follows local conventions for formality, salutations, and formatting.
With gated_send, a bilingual reviewer verifies translation accuracy, cultural appropriateness, and technical correctness before the response reaches the customer.
import requests
API = "https://api.multimail.dev/v1"
HEADERS = {"Authorization": "Bearer mm_live_xxx"}
def handle_multilingual_email(email: dict):
"cm"># Detect language from email body
language = detect_language(email["text_body"])
"cm"># Translate question to English for processing
question_en = translate_to_english(email["text_body"])
"cm"># Find answer in knowledge base
answer_en = search_knowledge_base(question_en)
"cm"># Translate response to customer's language
response = translate_to_language(answer_en, language)
# Send reply in customer's language
return requests.post(
f"{API}/reply",
headers=HEADERS,
json={
"in_reply_to": email["id"],
"text_body": response,
"html_body": f"<p>{response}</p>"
}
).json()
# Process unread emails
inbox = requests.get(
f"{API}/mailboxes/support-mailbox-id/inbox",
headers=HEADERS,
params={"status": "unread"}
).json()
for msg in inbox["emails"]:
email = requests.get(f"{API}/emails/{msg[&"cm">#039;id']}", headers=HEADERS).json()
handle_multilingual_email(email)Auto-detect the customer's language and draft a response in that language.
import requests
API = "https://api.multimail.dev/v1"
HEADERS = {"Authorization": "Bearer mm_live_xxx"}
def tag_by_language(email_id: str, text: str):
language = detect_language(text)
language_map = {
"es": "spanish", "fr": "french", "de": "german",
"pt": "portuguese", "ja": "japanese", "zh": "chinese"
}
tag = language_map.get(language, f"lang-{language}")
requests.post(
f"{API}/emails/{email_id}/tags",
headers=HEADERS,
json={"tags": [f"language-{tag}", "needs-bilingual-review"]}
)
return language
"cm"># Route to bilingual reviewer
lang = tag_by_language(email["id"], email["text_body"])
print(f"Tagged as {lang}, routed to bilingual reviewer")Classify and tag support emails by detected language for routing to bilingual reviewers.
"cm">// Multi-language support via MCP
"cm">// 1. Read incoming email
const email = await mcp.read_email({ email_id: inboxMsg.id });
"cm">// 2. Detect language and tag
const language = detectLanguage(email.text_body);
await mcp.tag_email({
email_id: email.id,
tags: [`language-${language}`, "needs-bilingual-review"]
});
"cm">// 3. Draft response in detected language
const answer = await getKnowledgeBaseAnswer(email.text_body);
const translatedAnswer = await translateTo(answer, language);
await mcp.reply_email({
email_id: email.id,
text_body: translatedAnswer
});Use MultiMail MCP tools for multilingual support workflows.
AI responds in the customer's language within minutes, eliminating the wait for a bilingual agent to become available.
AI adapts formality level, greeting conventions, and formatting to match cultural expectations — not just word-for-word translation.
Gated send ensures a bilingual reviewer verifies translation quality before customers see the response, catching nuances that AI translation might miss.
Support customers in 20+ languages without hiring native speakers for each one. One bilingual reviewer can handle multiple languages more efficiently.
Email infrastructure built for AI agents. Verifiable identity, graduated oversight, and a 38-tool MCP server. Formally verified in Lean 4.