AI search visibility for brands in India: a 2026 AEO guide

India is one of the largest AI-search audiences in the world, and its answers increasingly run in Hindi and regional languages, not only English. For brands, that is both an opening and a trap: the audience is already there, but an English-only presence will not reliably get you named. Here is how Indian brands earn a place in the answer.

India is where AI search gets its second-biggest audience

India is not a market you can treat as an afterthought in an AI-search strategy. In February 2026, ahead of the India AI Impact Summit, OpenAI's Sam Altman said India had become ChatGPT's second-largest market, with 100 million weekly active users, and the largest population of student users of any country. That is a vast, young, fast-growing base of people who now open an assistant before they open a search box.

The pull is not only ChatGPT. India runs overwhelmingly on Android - StatCounter put Android at roughly 92 percent of the country's mobile operating system market in mid-2026 - which puts Google's Gemini and AI answers within one tap of hundreds of millions of phones. Google brought its AI Mode to India in June 2025 and has since widened it, and AI Overviews already surface for a large share of Indian queries. So the answer layer here is genuinely split: a huge ChatGPT user base on one side, Google's Android-native reach on the other. A brand that is present in one and absent from the other is only half-visible.

The number worth internalising is not the raw user count. It is that a meaningful and rising share of buyer research in India now runs through an assistant that names a few options and moves on. If your brand is not one of the names, the buyer often never learns you exist.

The thing most brands get wrong: India is not one language

India's internet is not an English internet with regional pockets. It is a multilingual internet in which English is one language among many. The IAMAI and Kantar Internet in India Report 2024 counted about 886 million active users in 2024 and projected the base past 900 million in 2025, with the report attributing much of the growth to demand for content in Indian languages rather than English. The next wave of users online is arriving in Hindi, Tamil, Telugu, Bengali, Marathi and more, not in English.

This matters for AI answers specifically, because engines increasingly assemble the answer in the language of the question. Google's Gemini app launched in India in June 2024 supporting English plus nine Indian languages - Hindi, Bengali, Gujarati, Kannada, Malayalam, Marathi, Tamil, Telugu and Urdu. Google's AI Mode arrived in India in English in June 2025 and added Hindi in September 2025. The direction is unmistakable: the answer a user in Jaipur or Coimbatore sees is being built, more and more, from sources in their own language.

"India's next hundred million buyers are not asking in English, and the answer they see will not be either."

So the English-only trap is sharper in India than almost anywhere. A brand can have a polished English site, strong Google rankings and years of English press, and still be entirely absent when a buyer asks the same question in Hindi. The English answer and the Hindi answer are assembled from different pools of source material. Winning one does not win the other.

Curious how AI engines describe your brand right now? Get a free visibility audit and see where you stand across ChatGPT, Gemini and Perplexity.

Why translation alone will not save you

The instinct is to run the English site through machine translation and call it localised. That fails for two reasons. First, a literal translation rarely matches how people actually ask. An Indian buyer does not phrase a query the way a translation engine renders an English sentence; they mix registers, use local product names, and often type in a blend of English and their own language. Content that ignores that phrasing misses the intent the engine is trying to match.

Second, and more importantly, being named in an AI answer is not only about your own page. Engines lean on corroboration - other trusted sources describing you the same way. If the only place your Hindi claim exists is one auto-translated page, and no Hindi-language review site, trade outlet or community repeats it, the engine has thin evidence to work from. It will reach instead for whatever native-language material is available, which may be a competitor, or a generic English fallback that leaves you out.

The fix is not volume. It is a small number of clear, native, intent-matched pages on the questions that matter, echoed by a few local sources the engines already trust.

What actually needs localising

The fundamentals of AEO do not change at the border. You still need to be clearly defined, well-sourced and corroborated. What changes in India is that intent is local and multilingual, and your facts have to answer it in the language the buyer uses.

• Language. State what you do, who you serve and why you are credible in the languages your buyers actually use - starting with Hindi and the regional languages that dominate your category and region, not English alone. This is the single highest-leverage step.
• Local intent. Indian buyers ask for the best option in India, expect prices in rupees, and care about local delivery, cash-on-delivery or UPI payment, GST invoicing, and support in their language. Answer those explicitly rather than leaving a global page to guess.
• Local corroboration. Indian review platforms, regional trade media, marketplace listings and community forums are the sources the engines lean on for an Indian answer. Being described consistently across them is what earns the citation.

Where the corroboration lives

Knowing that engines want local corroboration is only useful if you know where to earn it. In India, the source pool an assistant draws on for a buying question tends to include a recognisable set: large marketplace listings and their reviews, category-specific comparison and review sites, regional-language news and trade outlets, YouTube (which is enormous in India and heavily indexed), and community discussion where buyers compare options in the open.

The practical move is to work backwards from the answer. Ask the engines the real questions your buyers ask, in the languages they ask them, and look at which Indian sources the answer actually cites. Those sources are already feeding the answer. They are your corroboration targets, because a consistent description of you across them is what turns a lonely page into a named recommendation.

A practical playbook for India

Nothing here requires a rebuild. It requires being deliberate about language, intent and corroboration in a market where all three are more fragmented than most brands assume.

• Publish your core facts in the right languages. Clear pages that state your offering, your market, your pricing basis in rupees and the use cases local buyers care about - in English and in the Indian languages that matter for your category, not machine-translated but written for how people ask.
• Cover both answer layers. ChatGPT's large user base and Google's Android-native Gemini and AI answers are separate battles. Check and earn your place in each rather than assuming one carries into the other.
• Earn Indian corroboration. The same clear description of you, echoed across the marketplaces, review sites, regional media and communities the engines already cite for your category.
• Answer the local sub-questions. Payment, delivery, warranty, GST invoicing, regional availability and support - answered on the page, in language, so the engine finds them when it fans a query out.
• Measure per language and per engine. Do not infer Hindi or Tamil visibility from your English numbers, and do not infer Gemini visibility from ChatGPT. Ask real buyer questions, per language and per engine, and check whether you are named.

What this looks like in practice

Picture a mid-sized appliance brand headquartered in Bengaluru. It has a strong English site, good Google rankings, and healthy reviews on the big marketplaces. When a buyer in Delhi asks ChatGPT, in English, for the best value water purifier under a certain budget, the brand sometimes appears. When a buyer in Patna asks Gemini the same thing in Hindi, it does not. The Hindi answer names two competitors instead, because their Hindi product pages state the specifics plainly and a couple of Hindi-language review videos and articles repeat them.

Nothing about the Bengaluru brand is worse. Its purifiers may well be better. But the Hindi answer was assembled from Hindi-language material, and the brand had almost none that spoke to the specific question. The fix is not a new product or a bigger ad budget. It is a clear set of Hindi pages that state what the product does, priced in rupees, with the sub-questions an Indian buyer asks - installation, service network, filter cost - answered on the same page, and a few local sources echoing it. Within a crawl cycle, the brand becomes a candidate the engine can name in Hindi too.

What this does not mean

It does not mean abandoning English. English remains vital in India - much business, much premium retail and a large share of professional research still happen in English, and English content earns you visibility for English queries and global audiences. The point is not English versus Indian languages. It is that the Indian-language half is the half most brands skip, and skipping it makes you invisible to a growing share of the local answer.

It also does not mean chasing every one of India's languages at once. Start with the languages that map to your category and your regions, prove the approach, and expand. A focused, well-corroborated presence in Hindi and two regional languages will beat a shallow auto-translation into twelve.

The takeaway

India offers a rare combination: one of the world's largest and youngest AI-search audiences, split across a huge ChatGPT base and an Android-native Google layer, in a market where the answer is increasingly assembled in the user's own language. Brands that publish clear facts in the right languages, earn Indian corroboration, and measure their visibility per language and per engine will be named while competitors are still relying on an English page and hoping it carries. The audience is already in the AI. The work is making sure the Indian answer - in whichever language it is spoken - knows who you are.