From Hinglish to Gujlish: Why Code-Mixed AI Is Now Mission Critical for CX
- Anand Shukla
- Jan 6
- 3 min read
If you listen closely to how India actually speaks, you’ll notice something important.
Nobody talks in clean, textbook language.
A customer might say, “Order cancel kari do, refund kab milega?”Or, in Gujarat, “Payment thayi gayu che, pan confirmation nathi aavtu.”
This isn’t broken language. This is the language.
Yet for years, customer experience systems have operated as if people speak in neatly separated boxes: English, Hindi, or Gujarati. Real conversations don’t work that way. And customers feel the gap instantly.
That gap is why code-mixed AI—systems that understand blended language like Hinglish or Gujlish—is no longer a “nice to have.” It’s becoming mission-critical for modern CX.
The Shift From Translation to Understanding
Early localization efforts focused on translation. Take an English sentence. Convert it into another language. Job done.
But translation alone doesn’t capture how people think or speak.
Code-mixing is not confusion; it’s efficiency. People borrow English words for speed, local words for emotion, and switch fluidly depending on context. Especially in digital interactions—search, chat, voice, support—this blended language is the default.
According to insights shared by the World Economic Forum, language accessibility is now a core pillar of digital inclusion. In markets like India, inclusion doesn’t mean “more languages.” It means more natural language use.
That’s a very different problem to solve.
Why “Gujlish” Matters More Than Perfect Gujarati
Let’s talk specifically about English to Gujarati translation, because it highlights the issue clearly.
A perfectly translated Gujarati sentence can still feel distant. Formal. Overly scripted. Many users, especially in commerce or support scenarios, prefer a mix: Gujarati structure with familiar English terms like refund, order, delivery, or OTP.
When CX systems fail to understand this mix, three things happen:
Queries get misclassified
Responses feel robotic
Users repeat themselves or drop off
None of this shows up as a “language error” on dashboards. But it shows up as friction.
As Harvard Business Review has repeatedly pointed out, customers don’t judge experiences in parts. They judge the whole flow. Language friction breaks that flow fast.
Five CX Realities Driving the Need for Code-Mixed AI
1. Customers Don’t Translate Themselves
People speak naturally. They don’t pause to decide which language a bot expects. CX systems that force this choice feel slow and unintuitive.
2. Search and Chat Are Already Code-Mixed
From Google searches to WhatsApp messages, mixed language is the norm. CX systems that rely on clean-language assumptions fall behind user behavior.
3. Regional Growth Relies on Comfort, Not Competence
As platforms grow to include more than just city dwellers, comfort becomes more important than features. Familiar linguistic patterns build trust faster than a slick UI.
4. Voice Interfaces Amplify the Problem
Voice inputs are even more code-mixed than text. An AI that can’t follow language switches in speech will struggle badly at scale.
5. Literal Accuracy Is Less Valuable Than Intent Accuracy
It’s more important to know what the customer means than to know every word. Code-mixed AI is more about intent than purity.
Deloitte’s research backs this change, saying that customer experience solutions based on real user behavior always work better than those based on idealized models.
What “Good” Code-Mixed CX Actually Looks Like
It’s not flashy.
A chatbot that understands: “Maro order return karvo che.”A support agent assist tool that suggests replies in natural Gujlish.A help article that doesn’t over-translate familiar English terms.
The experience feels human because it mirrors how customers already speak.
Some language infrastructure platforms, including Devnagri, have started addressing this by training models specifically on Indian code-mixed data, rather than forcing global language standards onto local behavior. The difference shows up not in demos, but in day-to-day interactions.
Actionable Takeaways for CX Leaders
Audit real customer conversations, not sample scripts
Measure drop-offs caused by misunderstanding, not just response time
Treat code-mixing as a signal, not a problem
Design language systems around intent, not grammar
Invest early—retrofits are expensive and messy
Language is not just about reach anymore. It’s about relevance.
A Closing Line Worth Remembering
Your customers are already speaking in Gujlish. If your CX can’t understand it yet, the problem isn’t their language. It’s your system.
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