Bot & AI
Your bot auto-detects whether the customer wrote in Hindi, Tamil, Spanish, or Hinglish — and replies in the same language. No language toggles, no per-region bots, no translation toolchains to maintain.
Languages
Auto-detected
Indian languages
Code-mix supported
Detection time
Per message
Bot to maintain
Not 40
Most WhatsApp platforms make you build a separate bot per language. That's 40x the maintenance, 40x the divergence risk, and a bad customer experience.
Each incoming message is classified by language before it reaches the bot. The bot then replies in the detected language using the same logic, knowledge base, and tools.
English, Spanish, Portuguese, French, German, Arabic, Mandarin, Japanese, Hindi, Tamil, Telugu, Bengali, Marathi, Gujarati, Punjabi, Kannada, Malayalam, Odia, Assamese, Urdu, and many more.
Hinglish ('mujhe loan chahiye urgently'), Tanglish, Spanglish, Portuñol — handled natively. Most platforms require pure-language input and break on code-mix.
Build your bot once with one system prompt in English (or any language). All languages inherit the same intent recognition, tool calls, and conversation flow.
Upload your help docs in English; the bot retrieves the right chunks and answers in the customer's language. No need to translate every document upfront.
Override specific phrases per language (e.g. 'check out' vs 'pagar' for Spanish e-commerce). Granular control where you need it, automatic everywhere else.
A customer in Mumbai writes 'kya mera loan approve hua' in Devanagari script. A customer in Mexico City writes '¿me llegó el préstamo?' in Spanish. Both come into the same bot.
Edesy's language classifier (built on fast multilingual models) tags the message with its language and confidence. It's robust on short messages, mixed scripts, and code-mix.
The same system prompt is interpreted by the LLM with explicit instruction to reply in the detected language. The LLM uses your knowledge base (in any source language) and answers natively.
The Mumbai customer gets a response in Hindi. The Mexico City customer gets it in Spanish. Same bot, same logic, same FAQ — different language. Both feel like they're talking to a local agent.
| Feature | Edesy | Typical competitor |
|---|---|---|
| Languages supported | 40+ | 5–10 |
| Auto language detection | ||
| Indian language code-mix (Hinglish) | ||
| Latin-script + Devanagari + Arabic | Latin only | |
| One bot for all languages | Per-language bot | |
| Knowledge base translates on the fly | ||
| Voice + multilingual combined | ||
| Per-language override controls | Hardcoded |
Customers in smaller cities prefer Hindi, Tamil, Telugu, Marathi. Bot auto-detects and serves each customer in their language without separate flows.
Conversion in Tier 2/3 cities up 2.1x vs English-only
Spanish (Mexico variant) vs Portuguese (Brazil) vs Spanish (Argentina) — all subtly different. Bot detects per-country style and responds appropriately.
One bot serves 8 countries with 100% language accuracy
Customers from France, Germany, Italy, Spain, Netherlands all message the same number. Bot replies in their native language for booking, support, modifications.
60% reduction in 'agent please' escalations
Customers in English, Afrikaans, Zulu, Xhosa. Bot handles all four natively. Calls routed to language-matched human agents only when needed.
Agent staffing cost down 40%
Students from India, Pakistan, Bangladesh, Indonesia, Egypt. Bot detects student's language, answers admissions questions, books demo classes in native language.
Demo-class booking rate 3x higher than English-only flow
Support inquiries from 30+ countries. Bot does tier-1 in customer's language, escalates to English-speaking agents only for complex cases (with bot translating the conversation history).
Tier-1 resolution rate up to 78% across all languages
When a company starts evaluating WhatsApp chatbot platforms, language support usually shows up in a feature matrix as a binary checkmark. 'Yes, we support multiple languages.' But what that actually means in practice varies wildly between vendors — and the difference shows up only when you've built your bot and discover it doesn't work for half your customer base.
There are three flavors of 'multilingual support' on the market. The cheapest is hardcoded translation: the platform stores N versions of every reply, and you pick which one to send based on a contact attribute (`contact.preferred_language`). This breaks the moment a customer writes in a language different from what's in their profile — which happens constantly in real life. The middle tier is 'choose a language at conversation start' (a list message asking 'English / Hindi / Tamil?'), which adds friction and still fails when the customer mid-conversation switches languages, which happens more than you'd expect in markets like India.
The right model is LLM-native language detection: the bot reads the message, identifies the language (including code-mix variants like Hinglish or Tanglish that 'pick a language' UIs can't handle), and responds in that language using the same underlying logic. Edesy uses this model. Your bot has ONE system prompt, written in any language you choose, plus instructions to mirror the customer's language. The LLM does the rest — language identification, in-context translation of your knowledge base content, and fluent response generation. The result is that your bot serves a Mumbai customer writing 'hi, mera order kahan hai?' the same way it serves a São Paulo customer writing 'oi, cadê meu pedido?' — instantly, natively, with no per-language configuration.
The practical upshot for Indian and LatAm businesses is enormous. Most Tier 2/3 customers prefer to write in their local language with English words mixed in. Most platforms can't handle that input style at all — they either misclassify the language or fail to understand the message. Edesy handles it natively because the LLM doesn't care about language boundaries the way rule-based systems do. For brands serving these markets, that's the difference between a chatbot that works for 30% of customers and one that works for 90%.
Set up your multilingual bot in your free workspace. Same effort as a single-language bot, infinitely more reach.