How does GEO work in India?

India is the second-largest ChatGPT market in the world — 100 million weekly active users, the largest student user base globally, and the number-one source of ChatGPT mobile downloads (13.7% of lifetime installs, ahead of the US at 10.3%). And Indian queries do not split neatly into "English" and "Hindi". A large share is Hinglish: Hindi sentence structure carrying English commercial vocabulary — brand names, product categories, "best", "price". Retrieval treats these as three different phrasings of the same intent, and often reaches three different source pools.

The practical consequence: a brand can rank well for English "best CRM in India" prompts and be absent from the Hindi and Hinglish variants that a much larger consumer audience actually types. Coverage has to be planned per mode, not per page.

Hindi is written in Devanagari, and that has a mechanical cost most teams never see: in UTF-8 each Devanagari character takes roughly three bytes, so the same content weighs about three times more than its English equivalent. Crawl budgets, context windows and machine-readable exports all feel it — we hit this on our own site, where the Hindi llms-full export forced us to double the size limit.

Devanagari also splits entities the same way Hebrew script does: models must learn that the Devanagari rendering and the Latin rendering of a brand are one thing. Indian sources habitually mix scripts mid-sentence, which helps — if your own pages declare both forms explicitly instead of leaving the mapping to chance.

Commercial patterns worth fixing in a battery: "best X in India" and city-level variants (Bengaluru, Mumbai, Delhi NCR), Hindi forms like "भारत में सबसे अच्छा X", and Hinglish constructions such as "X ke liye best agency". Price-sensitivity phrasing ("under ₹…", "sasta aur accha") appears far more often than in Western markets and pulls different sources into answers — the sensitivity is structural enough that OpenAI built a dedicated sub-$5 ChatGPT Go tier for India, then made it free for a year.

Our own case study showed assistants using the "…in India" qualifier when comparing vendors for this market — the same mechanism works for your category. A serious program samples all three modes on a schedule and reports share of voice per mode, because the curves move independently.

India's corroboration landscape is broad but noisy: business directories, review platforms, tech media, YouTube and community forums all feed assistant answers. Consistency beats volume — the same one-paragraph description of the brand, with both script renderings of the name, repeated across a curated set of surfaces, moves recognition more than scattered mentions.

For brands selling across India, English remains the authority backbone while Hindi content wins the consumer-intent citations. The two layers must say the same thing: assistants notice cross-language contradictions, and our measurement treats language consistency as a first-class metric.

We write Hindi natively in Devanagari with correct hreflang — no transliterated shortcuts, no machine-translation register — and keep every claim aligned with its English counterpart. Our own site runs this way in nine languages, Hindi included; the method and its verification are documented in our public case study.

Measurement covers the full spectrum: a fixed prompt battery in English, Hindi and Hinglish, citation and share-of-voice tracking per assistant, and the language-consistency check that catches drift between versions before it costs citations.