As businesses expand globally, delivering localized and multilingual customer support becomes essential to maintaining a strong customer experience. For companies looking to scale their customer interactions across languages, AI-powered solutions must be versatile and customizable.
According to a report by Common Sense Advisory, companies that localize their content and services are 1.5 times more likely to see an increase in revenue compared to those that do not.
Tiledesk’s suite of tools offers powerful options to create multilingual AI agents, from leveraging Large Language Models (LLMs) for dynamic prompts to implementing static solutions with JSON and visual automation.
In this article, we’ll explore three different ways you can build multilingual AI agents with Tiledesk, highlighting the pros and cons of each approach. And the best part is that each solution comes with a free and customizable template, which you can import into your project
The challenge: Meeting customers in their preferred language
Today’s customers expect personalized and immediate responses across channels, whether they’re browsing a website, messaging on WhatsApp, or using other platforms. A key challenge in achieving this is ensuring that AI agents can support multiple languages accurately and efficiently.
Multilingual customer support is crucial for maintaining a strong customer experience. Tiledesk addresses this challenge with a range of multilingual support solutions, from AI-driven prompt chains to more static setups that rely on Reply Filters or JSON-based translations.
Each approach offers unique benefits, whether your focus is flexibility, scalability, or ease of implementation.
Solution 1: Leveraging LLMs for dynamic multilingual customer support
If your goal is to offer dynamic and natural language support in multiple languages, Tiledesk’s Large Language Models (LLMs) integration is ideal. By creating a chain of prompts, you can set up interactions that adapt to the user’s language preferences, delivering real-time, personalized responses.
For example, an AI agent can detect a user’s preferred language based on browser settings or country code in non-browser channels like WhatsApp. By incorporating LLMs, these agents can generate natural responses in the detected language, providing a smooth multilingual experience.
-
- Example template: Check out our Multilingual AI Agent Template, which uses LLMs to generate personalized language-specific greetings and support messages.
Solution 2: Creating multilingual agents with Reply Filters
For businesses seeking more control over specific responses, the Reply Filter method allows you to define and manage static multilingual replies. Using the “user language” attribute, you can set up filters that display only the appropriate content based on the user’s detected language.
This approach is highly structured and involves organizing replies based on the languages you want to support, such as English, Italian, and Spanish. For example, you might set up the following responses:
-
- Welcome Message: A basic greeting customized for each supported language.
- Sales-Specific Welcome: A language-specific greeting for sales-related inquiries.
- Support-Specific Welcome: A customized greeting for support queries.
- Fallback Message: A default message for unsupported languages, where English acts as the pivot language.
Each message is set up using a Reply Filter, with language-specific conditions for displaying the correct version. A regular expression like ^((?!it|es).)*$ can be used to set English as the default fallback language if the user’s language isn’t directly supported.
-
- Example template: For more details, check out our Multi-language Chatbot Example Using Reply Filters.
Solution 3: Speeding up multilingual setup with JSON
For a streamlined approach to multilingual support, using JSON to store translations can be a game-changer. This method centralizes all language-specific responses in a single JSON file, allowing your AI Agent to pull content dynamically based on the user’s language.
Here’s a basic JSON structure to organize translations for multiple languages:
{
“en”: {
“welcome”: “Welcome”,
“support_button”: “Support”,
“sales_button”: “Sales”,
“welcome_support”: “Welcome to support”,
“welcome_sales”: “Welcome to sales”,
“fallback_message”: “I didn’t understand”
},
“it”: {
“welcome”: “Welcome IT”,
“support_button”: “Support IT”,
“sales_button”: “Sales IT”,
“welcome_support”: “Welcome to support IT”,
“welcome_sales”: “Welcome to sales IT”,
“fallback_message”: “Non ho capito”
},
“es”: {
“welcome”: “Welcome ES”,
“support_button”: “Support ES”,
“sales_button”: “Sales ES”,
“welcome_support”: “Welcome to support ES”,
“welcome_sales”: “Welcome to sales ES”,
“fallback_message”: “No entendía”
}
}
This JSON-based setup is efficient and easily scalable. Simply add or update translations in the JSON file as needed, making it ideal for managing a growing number of languages.
- Example template: For a step-by-step guide, see our Multi-language Chatbot Using JSON for Translations.
Which approach is right for you?
Choosing the right approach depends on your specific business needs and technical resources. Here’s a quick comparison:
-
- LLMs: Ideal for dynamic, natural conversations in multiple languages, especially useful if you need flexibility and real-time responses.
- Reply Filters: Best for those looking to control specific replies based on user language with a straightforward and reliable setup.
- JSON: A fast, scalable option for managing static multilingual content centrally, perfect for businesses needing a simplified setup that can grow with their needs.
By exploring Tiledesk’s range of multilingual tools, you can provide a localized and engaging experience for customers worldwide. Whether you’re harnessing the power of LLMs, structuring responses with Reply Filters, or centralizing content in JSON, Tiledesk’s platform offers a tailored solution to meet your multilingual support goals.
