Open your support inbox and read how customers actually write. "Bhaiya delivery kab tak?" "Return karna hai size chota hai." "Pongal offer irukka?" Almost none of it is the polished English your FAQ page was written in - and that mismatch is where most chatbots quietly fail.
The English-only trap
Traditional keyword bots match exact phrases. If your FAQ says "What is your return policy?" and the customer types "return kaise hoga," the keyword bot shrugs. So businesses either force customers into rigid button menus, or watch queries fall through to a human - defeating the point of automation entirely.
The uncomfortable truth: a bot that only understands formal English only serves the customers who least need help. The customer who writes in Hinglish or a regional language is exactly the one who won't email you a neatly formatted question later.
How semantic understanding changes this
Modern retrieval doesn't match words - it matches meaning. "Return kaise hoga," "how to send back," and "exchange karna hai" all land on the same passage of your return policy, even though they share almost no words with it. Your knowledge base stays in whichever language you wrote it; the understanding layer handles the variety on the customer's side.
Your documents can be in English. Your customers shouldn't have to be.
Answering in the customer's register
Understanding the question is half the job; the reply matters too. A good agent mirrors the customer's register - a Hinglish question gets a friendly Hinglish-flavoured answer, a formal English enquiry gets a formal reply - while the facts underneath stay grounded in your documents and cited. Tone adapts; truth doesn't.
As regional-language models improve, this extends naturally to Tamil, Telugu, Bengali, and beyond. The businesses that win those customers will be the ones whose support never asked them to translate themselves first.