Type this into almost any AI chatbot: “Kal subah 9 baje ka reminder laga do, aur mummy ko bhi bata dena.”
Two years ago, many tools would have stumbled. Mixed Hindi and English, written in the Roman script, with a casual family reference, was exactly the kind of sentence that confused them.
Today most good assistants handle it fine. That one small change tells you a lot about how far Indian-language AI has come. It also hints at how much further it still has to go.
The real problem was never just translation
India has 22 scheduled languages, hundreds of dialects, and a habit of mixing them freely in the same sentence. On top of that:
- People write Hindi in the Devanagari script, in Roman letters (“Hinglish”), and sometimes in both in one message.
- Online text in languages like Odia, Assamese or Maithili is tiny compared with English, so models had little to learn from.
- A large share of Indians are more comfortable speaking than typing, especially in their own language.
So good Indian-language AI needs three things: understanding code-mixed text, working with very little data, and handling voice well. For a long time global models were weak at all three.
What has improved in 2026
Homegrown models. The biggest shift this year has been Indian companies training their own models instead of just fine-tuning foreign ones. At the India AI Impact Summit in February, Sarvam AI, Gnani.ai and BharatGen unveiled new indigenous models across voice, language and real-world use (Sify). Sarvam’s lineup included 30-billion and 105-billion-parameter models that it says were trained from scratch, not built on existing open-source systems (TechCrunch).
Voice first. This matters most for ordinary users. Sarvam has built its models to work through voice commands in 22 Indian languages (ThePrint). A shopkeeper who never types can now just talk.
Public infrastructure. The government’s BHASHINI platform now offers more than 20 language services, like language detection and speaker identification, across 36 text and 23 voice languages (News On AIR). It also hosts open-source Sarvam models, so smaller developers can build on them.
Reading documents. Scanned forms, old government records and printed books in regional scripts used to be a nightmare for OCR. Newer vision models handle them far better, which matters a lot for land records, court papers and school material.
Language by language: an honest feel
There’s no official scorecard, but here is how it feels from everyday use:
| Language | How it feels today |
|---|---|
| Hindi (Devanagari and Hinglish) | Very good. Casual chat, drafting and summarising all work well |
| Bengali, Tamil, Telugu, Marathi | Good for everyday use. Formal writing can still sound stiff or textbook-like |
| Kannada, Malayalam, Gujarati, Punjabi | Decent and improving fast. Idioms and humour often get lost |
| Odia, Assamese, Maithili, Konkani and smaller languages | Usable for basics. Mistakes are common, and voice support varies |
| Tribal and unwritten languages | Still very early. Most work is happening in research projects |
Where it still falls short
Tone and respect. Indian languages encode respect in grammar. Aap, tum and tu are not interchangeable. AI often picks the wrong level, which can sound rude or oddly stiff.
Local knowledge. A model might translate a sentence perfectly and still miss what a Tamil proverb or a Bhojpuri joke actually means.
Accents and noise. Voice AI does well in a quiet room. On a busy street, a bus or a crowded sabzi mandi, accuracy drops.
Bias toward the big languages. Hindi and the larger southern languages get most of the attention. Speakers of smaller languages still get a second-class experience.
Why this matters beyond technology
This is not just a tech story. For a farmer checking a government scheme, a mother reading a prescription, or a student preparing for an exam in their mother tongue, good language AI is the difference between access and exclusion.
It’s also a big business opportunity. Apps, banks and e-commerce companies that talk to customers in their own language see better trust and more engagement. If you’re building a product for Bharat rather than just metro India, multilingual support is now possible and affordable. MindStick’s mobile app development and AI services teams can help you build it in.
Tips if you use AI in your own language
- Write the way you naturally speak. Hinglish is fine, and models understand it well now.
- For formal documents, ask the AI to use “formal” or “shuddh” language. Otherwise it may mix registers.
- Always double-check names, numbers and legal terms in the output.
- Try voice mode. In many Indian languages it now works better than typing.
The road ahead
The gap between English and Indian languages in AI is narrowing, but it isn’t closed. The next few years will likely bring better voice support in smaller languages, stronger local knowledge and cheaper on-device models that work offline. That last one matters a lot for rural India. We explained why in our post on small language models.
The day a grandmother in a village can ask her phone, in her own dialect, whether her pension has arrived, and get a correct answer, Indian-language AI will have truly arrived. We’re closer than ever, but not quite there.
Which Indian language do you use with AI, and how well does it work for you? Tell us on MindStick YourViews, or ask a question on MindStick Answers.
