A tutorial for setting up an AI assistant that automatically detects and responds in your customers' languages without manual language switching.
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The most common misconception about multilingual AI is that you need to translate your entire knowledge base into every language you want to support. With Eaxy, this is not necessary. The AI model can take your business knowledge in one language and generate accurate, natural-sounding responses in dozens of other languages. Start by ensuring your primary-language knowledge base is comprehensive and well-organized. Include all the information the assistant needs to answer customer questions: services offered, pricing, business hours, frequently asked questions, policies like cancellation and refund rules, and any technical specifications or product details. The quality of multilingual responses is directly proportional to the quality of your source material. If your English knowledge base has vague or incomplete answers, the translations into other languages will inherit those weaknesses. Once your primary knowledge base is solid, identify terms that should not be translated or that require specific translations. Brand names, product names, and certain industry-specific terms may need to remain in the original language or use an official translation rather than a machine-generated one. For example, a restaurant might want "Tarta de Santiago" to stay in Spanish even when responding in English, rather than being translated to "Santiago Cake." Create a terminology glossary with these protected terms and their approved translations in your most common customer languages. This glossary acts as an override for the AI translation layer, ensuring brand consistency and accuracy for your most important vocabulary.
Tutorials
While the AI handles language detection and response generation automatically, there are configuration options that let you fine-tune how your multilingual agent behaves. First, decide whether the assistant should always respond in the detected language or if there are cases where it should default to a specific language. For most businesses, always matching the customer language is the right choice. But some businesses — particularly those with legal or regulatory requirements — may need the assistant to switch to an official language for certain types of responses like terms and conditions or medical disclaimers. Configure these exceptions in the language behavior settings. Second, set up language-based routing for human handoffs. When the assistant escalates a conversation to a human, it matters which team member receives it. If a customer was communicating in French and the conversation gets routed to a team member who only speaks English, the handoff creates a negative experience. Configure routing rules so that French conversations go to French-speaking team members, Spanish conversations go to Spanish speakers, and so on. If you do not have native speakers for every language, configure a fallback that routes less common languages to a team member who can use translation tools or to the most multilingual person on the team. Third, set the assistant greeting behavior. When a customer initiates a conversation, should the assistant greet them in a default language and then switch after detecting theirs, or should it use a neutral greeting that works across languages? The cleanest approach is a brief neutral greeting followed by immediate language matching once the customer sends their first substantive message.
Tutorials
Testing a multilingual agent requires more effort than testing a single-language assistant, but the investment prevents embarrassing errors that could damage your brand in specific language markets. Start by identifying your top three customer languages based on your existing inquiry data. If you do not have data yet, use your geographic location and target market to make an informed estimate. For each language, recruit a native speaker to test the assistant — this can be a team member, a friend, or a freelancer. Machine-generated responses can contain subtle errors that only native speakers catch, like unnatural phrasing, incorrect formality levels, or culturally inappropriate expressions. Have each tester run through the same set of scenarios: a greeting, a question about services, a pricing inquiry, a booking request, a complaint that should trigger escalation, and a message that references your protected terminology from the glossary. For each scenario, the tester should evaluate whether the response sounds natural in their language, whether the information is accurate, whether the tone matches your brand, and whether protected terms are translated correctly or left in the original language as intended. Document any issues and adjust the knowledge base or terminology glossary accordingly. After fixing issues, re-test until the quality meets your standards for each language. Once the top three languages are validated, expand testing to additional languages as resources allow. Languages with smaller customer volumes can be monitored through periodic spot-checks rather than full test cycles. Set up a monthly review process where you sample conversations from each active language and check response quality, catching any drift or degradation before it becomes a pattern.
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Explains how Eaxy auto-detects customer language and responds fluently in over fifty languages without manual configuration.
A tutorial for turning FAQs and service information into better AI behavior.
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