DEV Community: ifham baig

How to Rank in Google AI Overviews

ifham baig — Thu, 01 Oct 2026 09:13:04 +0000

Originally published on depra.ai on 15 September 2026.

Google's AI Overviews, the AI-written summaries above the normal search results, link to pages in Google's index. They do not need special markup or AI-specific files. We captured 60 Google results pages from India on 15 September 2026 for 10 shopping queries, each in English and in Hinglish (Hindi and English mixed, typed in Latin letters). Hinglish searches showed an overview in 28 of 30 captures, and English searches in 18 of 30.

What decides which pages AI Overviews cite?

According to Google's AI features documentation, a page must be "indexed and eligible to be shown in Google Search with a snippet" to appear as a supporting link. No special schema.org markup or machine-readable file is required.

Ordinary SEO is still the foundation: let Googlebot crawl the page, link internally, put the important content in text, use good images and video, keep structured data consistent with the visible page, and keep merchant and business profile information current.

Google also uses "query fan-out", running related subtopic searches to build a fuller answer. That means a page can be cited even when it does not rank for the exact search term.

In our dataset of 41 overviews that loaded, there were 274 distinct linked pages:

95 links (34.7%) pointed to pages that ranked on page one for the same search.
40 of 41 overviews included at least one page-one result.
16 of 41 linked the top-ranking regular result.
In English searches, 26.7% of links were page-one results. In Hinglish, 40.9% were.

Even so, 179 of the 274 links went to pages beyond page one, which fits Google's description of fan-out.

How often do AI Overviews show for Indian shopping questions?

We used 10 shopping queries (5 skincare, 5 fashion) with no brand names, each in English and Hinglish, and captured each three times: desktop, mobile, and desktop again.

Question (English wording, shortened)	English	Hinglish
Face wash for oily skin under ₹500	3 of 3	3 of 3
Sunscreen with no white cast	3 of 3	3 of 3
Vitamin C serum for dark spots	0 of 3	3 of 3
Moisturizer for dry skin in winter	3 of 3	2 of 3
Serum for acne-prone skin under ₹700	3 of 3	3 of 3
Men's jeans under ₹2000	1 of 3	3 of 3
Running shoes under ₹3000	2 of 3	3 of 3
Women's kurta sets online	1 of 3	3 of 3
Men's formal shirts	1 of 3	3 of 3
Wedding sarees on a budget	1 of 3	2 of 3
Total	18 of 30	28 of 30

Three patterns stood out:

English fashion queries rarely produced an overview. English skincare showed an overview in 12 of 15 captures, and English fashion in only 6 of 15. Hinglish was steady at 14 of 15 in both categories.
English pages without an overview showed a product carousel instead. All 12 English captures with no overview had Google's product carousel with prices. No Hinglish capture showed that carousel.
The same query gave different results between captures. Four English questions produced an overview in only 1 of 3 captures, taken roughly 20 minutes apart.

Which sites did AI Overviews cite?

The 41 overviews with readable links cited 68 distinct websites. These are the top 10:

Site	Overviews citing it (of 41)	English (of 16)	Hinglish (of 25)
youtube.com	18	7	11
amazon.in	17	7	10
nykaa.com	16	8	8
flipkart.com	15	7	8
reddit.com	11	5	6
myntra.com	10	1	9
instagram.com	9	5	4
dotandkey.com	6	6	0
plumgoodness.com	6	6	0
shop.skinaa.com	6	6	0

In total, 34 of 41 overviews cited at least one marketplace (Amazon.in, Flipkart, Nykaa or Myntra). Video or social platforms appeared in 24 of 41, and community platforms (Reddit or Quora) in 14 of 41.

Of the 274 linked pages, 75 (27%) sat under blog paths and 76 (28%) had "best" or "top" in the URL. Buying guides dominated, with addresses like "10-affordable-face-serums-under-500-in-india" and "top-jeans-brands-in-india-men". Brand sites were cited too: Skinaa, Dot & Key and Plum each appeared in 6 English overviews. Skinaa was linked through its own blog buying guides, while Dot & Key and Plum were mostly cited through category pages that matched a buyer need.

Two language-specific observations:

Hinglish overviews were mostly written in Hindi script. 21 of 25 Hinglish overviews with loaded text were mainly in Devanagari, even though the searcher typed in English letters.
Google translated English pages for Hinglish searchers. 19 of 25 Hinglish overviews linked at least one English page through Google Translate, which was 49 of 154 links.

Google's shopping listings also showed up inside answers: 17 of 18 English overviews and 16 of 25 Hinglish overviews contained product cards that open Google's shopping product viewer.

8 steps to get your pages into AI Overviews

Let Googlebot crawl the page. AI Overviews link to pages in Google Search's index, which Googlebot fills. Check robots.txt, firewall settings and CDN configuration, then test with URL Inspection in Search Console.
Treat Google-Extended separately. Google's documentation says Google-Extended controls whether content is used for Gemini training. It does not affect inclusion in Google Search and is not a ranking factor, so disabling it will not remove you from overviews.
Keep snippets working. Google's eligibility rule needs a page that can be shown with a snippet. A nosnippet tag or a restrictive max-snippet value limits what Search can show, so use them only on pages you really want to exclude.
Rank well for the exact query. In our data, 40 of 41 overviews included page-one results. Compelling titles, intent-matched pages and internal links from relevant content still matter.
Answer directly in the opening text. Put the main answer in the first two sentences and the supporting detail after it. Google treats text-based content as a core practice, and answers that exist only in images or video are harder to quote.
Write real buying guides. 28% of linked pages had "best" or "top" in the URL. Include product names, prices, who each product suits, and publication dates. Brand-run pages can be cited too: Dr. Sheth's sunscreen guides and Plum's oily-skin collections both received links.
Be present on the platforms that get cited. Marketplaces appeared in 34 of 41 overviews, YouTube or Instagram in 24, and Reddit or Quora in 14. Keep your Amazon.in, Flipkart, Nykaa and Myntra listings complete, keep Merchant Center data current, publish on YouTube, and answer buyer questions on Reddit.
Test Hinglish searches separately. Hinglish showed overviews in 28 of 30 captures against 18 of 30 for English, and 40.9% of Hinglish links were page-one results against 26.7% for English. If your audience searches in Hinglish, test it on its own, and keep your English guides translation-friendly: shorter sentences, product names in text, and prices as numbers.

How to check whether an AI Overview shows for your queries

A manual check:

Open a private browser window so your history does not influence the results.
Go to google.co.in and type the question exactly as customers would, in English and in Hinglish.
Note whether an overview appears. If it does, expand it and record every linked site.
Repeat on mobile and later in the day. Five of our 10 English questions changed between the 3 captures.

Search Console only gives aggregate data. Google counts AI features traffic inside the overall Performance report, under the Web search type.

Manual checks stop being practical beyond a handful of queries. Depra AI's Google AI Overview tracker monitors buying questions daily on real Google results pages from your location, and records whether an overview appeared, whether your brand was mentioned, and which pages were linked, in English and Hinglish. Overview frequency is tracked separately from brand visibility, so a week with fewer overviews does not look like a drop in visibility.

What are the limits of this data?

One day, one short window. The 60 pages were captured on 15 September 2026 between 13:50 and 14:11 IST. Google changes when it shows overviews often, so read this as a snapshot.
Ten questions in two categories. Skincare and fashion buying queries with no brand names. Informational or local searches may behave differently.
India, logged out, country level. Searches ran from India with an English interface and no account. Searchers in other regions, or signed in, may see different results.
Three captures per version. Desktop, mobile and a desktop repeat show the variation but are not enough for a precise rate.
Retrieval gaps in the final pass. Three Hinglish overviews appeared with no text or links loaded, and two Hinglish pages showed no overview. Retrieval errors may mean the Hinglish count is slightly underestimated. Empty overviews count in the appearance tables but are left out of the citation analysis.
How pages were counted. Google adds tracking tags (srsltid) to many shopping URLs, and they differ between links. We removed them before matching so each page counted once.
What "page one" means. Seven to nine regular results, with the rest of the page taken up by overviews, videos, product rows and "People also ask" boxes.
Links show citation, not causation. The counts show which sites and page types were linked. They do not prove that any specific change will get you included.
Funding. Depra AI ran and paid for this analysis, and also sells AI Overview tracking.

You can start 7 days free on any plan, no card needed, and the first scan starts on signup.

FAQ

How do I get my website into Google AI Overviews?
Make sure Googlebot can crawl the page, that it is indexed, and that it can be shown with a snippet in normal Google results. Answer the exact question in plain text near the top of the page. There are no extra requirements, special markup or AI files. In our 15 September 2026 data, 40 of 41 overviews included a page-one result, which suggests traditional ranking still works.

Does schema markup help with AI Overviews?
There is no special schema.org requirement for inclusion. Google asks that any structured data you use matches the visible page. Structured data is still useful for other Google results such as product information, so keep it accurate.

Does blocking Google-Extended remove me from AI Overviews?
Google says Google-Extended does not affect Search inclusion or ranking signals. Overviews are part of Google Search and link from the standard index. To limit how you appear in Search, Google recommends nosnippet, data-nosnippet, max-snippet and noindex controls instead.

Do AI Overviews show for Hinglish searches?
Yes, and more often than for English in our test. Data from 10 Indian shopping questions on 15 September 2026 showed overviews in 28 of 30 Hinglish captures against 18 of 30 English ones. Most Hinglish overviews were written in Devanagari Hindi even though the question was typed in English letters.

Depra AI provides AI visibility tracking for Indian brands. Track ChatGPT, Gemini, Perplexity and Google AI Overview mentions in English and Hinglish from ₹1,999 monthly. India-based, globally operational.

What Is llms.txt and Does It Work?

ifham baig — Thu, 01 Oct 2026 09:10:01 +0000

Originally published on depra.ai on 15 September 2026.

llms.txt is a plain-text proposal: a file at the root of your website that points AI tools to your most important pages. We looked at whether it actually does anything. The short version: 53% of the sites cited in our India shopping study have some kind of llms.txt, but none of the major engines shows evidence that it influences which sites they cite, and Google says outright that it ignores the file.

What is llms.txt?

llms.txt is a markdown file served at yourdomain.com/llms.txt. It holds the site name, a short summary and a curated list of page links. Jeremy Howard published the proposal on 3 September 2024, and version 2 followed on 10 August 2026. The only required element is a markdown heading. Optional parts include a summary, lists of links, and an "Optional" section for AI tools that are short on space.

Does llms.txt work?

Here is what the main platforms say, and what we saw.

Google: its AI optimisation guidance says Google Search ignores these files, and that they neither help nor harm visibility.
OpenAI and Perplexity: both point site owners to robots.txt for crawler access, and neither mentions llms.txt.
Chrome Lighthouse: it includes llms.txt audits, but they do not score rankings.
Depra's data: adoption among the 10 most-cited sites was mixed. YouTube (100 citations) has no file, and the most-cited sites did not line up with having one.

What we found in the Indian shopping study

We analysed 480 AI shopping answers (ChatGPT, Gemini and Perplexity) from India. They cited 247 websites, and we also checked 50 major brand sites.

Category	Sites	Custom-written	Shopify default	Generated	No file
Cited websites	247	40 (16.2%)	89	3	90
Brand sites	50	7 (14%)	31	0	10

Overall, 53% of cited sites and 76% of brand sites have a file. But most of those files are Shopify defaults with very little brand-specific information in them.

The 10 most-cited websites

Website	Citations	llms.txt status
reddit.com	106	Could not check
youtube.com	100	No file
nykaa.com	93	Could not check
flipkart.com	79	Could not check
amazon.in	79	Could not check
instagram.com	67	No file
smytten.com	52	Written for the site
ndtvshopping.com	49	Could not check
hindustantimes.com	48	Written for the site
myntra.com	47	No file

llms.txt vs robots.txt

	robots.txt	llms.txt
Status	Web standard (RFC 9309)	Proposal (2024 / 2026)
Function	Allows or blocks crawlers by path	Suggests priority pages
Enforcement	Search crawlers honour it	No confirmed enforcement
AI impact	Blocking crawlers prevents indexing	No confirmed effect on citations

How to write an llms.txt

Pick the pages. 5 to 20 URLs covering collections, guides, shipping, returns and contact.
Format it. Start with a # heading (your brand name), add a summary after >, and group links under ## headings.
Publish it. Put it at the domain root as plain text, and check it by visiting the URL directly.
Mind your platform. Shopify needs templates/llms.txt.liquid in the theme. WordPress plugins such as Rank Math can generate the file. Custom sites need a static file in the public folder.
Maintain it. Update the file when pages move or collections are renamed.

Example file

# Example Skin

> Sunscreens and serums made
> for Indian weather.

## Shop

- [SPF](https://example.com/spf)
- [Serum](https://example.com/serum)

## Help

- [FAQ](https://example.com/faq)
- [Returns](https://example.com/ret)

## Optional

- [Blog](https://example.com/blog)

Should you add one?

It is optional, and the benefit for AI visibility is unproven. If you have limited time, do these first:

Allow search crawlers in robots.txt (OAI-SearchBot, PerplexityBot, Googlebot).
Publish content that answers buying questions: comparisons, guides and product pages.
Get mentioned on sites AI already cites: Reddit, YouTube, news sites and marketplaces.
Measure over weeks, because AI answers fluctuate.

Depra AI tracks how ChatGPT, Gemini, Perplexity and Google AI Overviews mention your brand, in English and Hinglish, so you can see whether any of this moves your numbers.

FAQ

What is llms.txt?
A plain-text markdown file at yoursite.com/llms.txt with a site summary and curated links. Jeremy Howard proposed it on 3 September 2024, and version 2 was released on 10 August 2026. No major AI search engine confirms that it uses the file for citation decisions.

Does Google use llms.txt?
No. Google Search ignores llms.txt, and Google says such files have no effect on rankings or visibility. Chrome Lighthouse audits the file separately but does not score rankings.

Does llms.txt help SEO?
There is no demonstrated effect on rankings or citations, and Google explicitly denies any impact. OpenAI and Perplexity recommend robots.txt instead. Treat it as optional, and put crawler access and quality content first.

Where do I put llms.txt?
At the domain root, as plain text: https://yourdomain.com/llms.txt. On Shopify, add templates/llms.txt.liquid to the theme. On WordPress, use an SEO plugin such as Rank Math to generate it.

Depra AI is AI visibility tracking software for brands in India, monitoring ChatGPT, Gemini, Perplexity and Google AI Overviews mentions in English and Hinglish from ₹1,999 a month. Based in India, working worldwide.

How to Write Hinglish Prompts for AI Tracking

ifham baig — Thu, 01 Oct 2026 09:06:12 +0000

Originally published on depra.ai on 15 September 2026.

To write effective Hinglish prompts for AI visibility tracking, type buyer questions the way Indian shoppers would, in English letters. Keep the product names, price limits, use cases and the type of request identical to the English version, and check that both versions ask exactly the same thing before you start tracking.

What is a Hinglish prompt?

A Hinglish prompt keeps the English product terms and writes the surrounding words in romanised Hindi. Here is a matching pair:

English: Which face wash should I buy for oily skin in India under ₹500?
Hinglish: India mein oily skin ke liye 500 rupees ke andar konsa face wash kharidna chahiye?

"Face wash", "oily skin" and "rupees" stay in English. "mein", "ke liye", "konsa" and "kharidna chahiye" are romanised Hindi. There is no Devanagari script anywhere, and both versions have the same product category, skin type, price cap and country.

Six rules for Hinglish tracking prompts

1. Keep the same meaning and the same request

Both versions must ask the same question. Our study prompts ask "what to buy", using "should I buy" in English and "kharidna chahiye", "kharidni chahiye" or "kharidun" in Hinglish. If the English version asks for a recommendation, a comparison or an explanation, the Hinglish version must ask for the same thing.

Example (F04)

English: Which brand's formal shirts should I buy for men in India?
Hinglish: India mein men ke liye konse brand ki formal shirts kharidni chahiye?

2. Hold every constraint identical

Price caps, gender, occasion, singular or plural, and country must match across the pair. Any difference in the answers should come from the language alone, not from a changed constraint.

Example (F01)

English: Which brand's jeans should I buy for men in India under ₹2000?
Hinglish: India mein 2000 rupees ke andar men ke liye konse brand ki jeans kharidni chahiye?

The study logged "₹2000" against "2000 rupees" as a deliberate choice, because that is how each language naturally writes prices.

3. Keep product nouns in English and use natural Hindi grammar

Keep category nouns like sunscreen and jeans in English. Map idioms rather than translating them literally: "acne-prone skin" becomes "acne wali skin". Check gender agreement too. In Hindi, verbs and particles (ka, ki, ke) change to match the noun's gender.

Example (S02)

English: I want to buy a sunscreen that doesn't leave a white cast on Indian skin. Which one is best?
Hinglish: Mujhe aisa sunscreen kharidna hai jo Indian skin par white cast na chhode. Konsa best hai?

4. Use English letters only

Write every Hinglish prompt in Latin script. A Devanagari prompt measures Hindi, not Hinglish. The 10 Hinglish prompts in the study contain no Devanagari characters.

5. Name no brand in either version

A question that contains a brand name cannot show whether engines recommend you on their own. None of the 20 study prompts named a brand.

6. Keep the Hindi share realistic and steady

Across the study's 10 Hinglish prompts, Hindi words made up between 43% and 69% of each prompt. Keep your own set similarly consistent, so that "Hinglish" means the same thing throughout your tracking.

Three prompt pairs that failed review

In the review on 14 August 2026, before any analysis, three fashion pairs failed validation. All three were corrected.

Sarees (F05): a market hint in only one version

Neither version originally mentioned India. But the Hinglish word "shaadi" signals an Indian wedding by itself, while the English "wedding" carries no market hint. That asymmetry could explain brand differences that have nothing to do with language.

Fixed pair

English: Which sarees should I buy online in India for a wedding on a budget?
Hinglish: India mein shaadi ke liye budget mein online konsi sarees kharidun?

Kurta sets (F03): one question asking two things

The original combined "where to buy" with "which brands", which invited retailer names (Myntra, Amazon, Nykaa Fashion) into the brand counts.

Fixed pair

English: Which brand's kurta sets should I buy for women online in India?
Hinglish: India mein women ke liye online konse brand ke kurta sets kharidun?

Formal shirts (F04): no buy verb

Neither version explicitly asked for a purchase recommendation, so both read as requests for an opinion. The fixed pair is the one shown under rule 1.

Handling changed prompts

Five answers collected under the old F03 and F05 wording were deleted before analysis, and the deletion was logged. The verification log and the locked prompt file are public on GitHub. When you revise a prompt's wording, compare only the answers collected under the new wording.

Hindi word share, prompt by prompt

In the study's 10 Hinglish buying prompts, Hindi words made up 42.9% to 69.2% of the words, 58.4% on average. Product nouns stay English, connecting words are Hindi.

Pair	Hinglish prompt	Words	Hindi words	Hindi share
S01 face wash	India mein oily skin ke liye 500 rupees ke andar konsa face wash kharidna chahiye?	14	8	57.1%
S02 sunscreen	Mujhe aisa sunscreen kharidna hai jo Indian skin par white cast na chhode. Konsa best hai?	16	10	62.5%
S03 vitamin C serum	Dark spots aur dull skin ke liye India mein konsa vitamin C serum kharidun?	14	6	42.9%
S04 moisturizer	Indian winters mein dry skin ke liye konsa moisturizer kharidna chahiye?	11	6	54.5%
S05 acne serum	India mein acne wali skin ke liye 700 rupees ke andar konsa serum kharidun?	13	8	61.5%
F01 men's jeans	India mein 2000 rupees ke andar men ke liye konse brand ki jeans kharidni chahiye?	14	9	64.3%
F02 running shoes	Mujhe India mein 3000 rupees ke andar running shoes kharidne hain. Konse lene chahiye?	13	9	69.2%
F03 kurta sets	India mein women ke liye online konse brand ke kurta sets kharidun?	12	6	50.0%
F04 formal shirts	India mein men ke liye konse brand ki formal shirts kharidni chahiye?	12	7	58.3%
F05 sarees	India mein shaadi ke liye budget mein online konsi sarees kharidun?	11	7	63.6%

Mixing inconsistently, say half your prompts at 30% Hindi and half at 80%, blends two different ways of asking into a single trend line.

Method note: the study split prompts on spaces, ignored punctuation and numbers, and counted romanised Hindi particles (mein, ke, liye, konsa) as Hindi. India, rupees, men, women, online, budget, best, brand and the product nouns counted as English. Moving a few words between the two classes changes the shares by 6 to 9 percentage points. The data covers 10 prompts, 2 categories, one fieldwork day and one Hinglish style that keeps product nouns in English. The study did not test whether a different Hindi share changes the answers.

How to check a Hinglish prompt before tracking

Run every pair through this checklist before you start:

Both versions make the same request (recommendation, comparison, explanation or purchase).
Neither version names a brand, store or competitor.
The country appears in both versions or in neither.
Price cap, gender, occasion and singular or plural all match.
No word in one version hints at a market or occasion that its pair lacks.
Product nouns stay in English, and verbs and particles agree with the noun's gender.
The Hinglish version has no Devanagari characters.
An independent reviewer confirms that both versions ask the same thing.
Record the start date. After any edit, compare only answers from the new wording.

Tracking Hinglish prompts in Depra AI

When India is your only market, Depra AI suggests 5 topics with 8 questions each, mostly buying questions without brand names, including Hinglish versions in English letters. For India-only markets some questions name India directly, and for multi-market projects none name a country. You can edit, pause or tag each prompt, so run the checklist above before you keep any suggestion.

Each language version uses one prompt slot per market. Starter at ₹1,999 a month (plus GST) includes 20 prompts, enough for 10 English and Hinglish pairs in one market. Growth at ₹7,499 includes 100.

Depra queries each version daily on ChatGPT with web search, Gemini and Google AI Overviews from inside India, and weekly on Perplexity, and records the language of the reply. Your report shows English and Hinglish visibility side by side. You can start 7 days free on any plan, no card required.

FAQ

Should Hinglish prompts be written in Devanagari?
No. Hinglish is Hindi and English typed in English letters, so use Latin script only. Devanagari prompts test Hindi, not Hinglish. The study's 10 Hinglish prompts contain no Devanagari, and Depra's suggested versions follow the same rule.

How much Hindi should a Hinglish prompt contain?
In the study's 10 Hinglish buying prompts, Hindi words made up 42.9% to 69.2% of the words, 58.4% on average. Keep product nouns in English, write the connectors in Hindi, and keep the mix consistent across your whole set.

Can I machine-translate English prompts into Hinglish?
Only as a rough first draft. Word-for-word output reads unnaturally, and a literal translation misses things like "acne-prone skin" becoming "acne wali skin". Rewrite the prompt the way a buyer would type it, check verb and noun gender agreement, and confirm that both versions mean the same thing.

Does a Hinglish version use a prompt slot in Depra?
Yes. Each language version is tracked on its own and takes one slot per market. Starter (₹1,999 a month plus GST) covers 10 English and Hinglish pairs in one market, and Growth (₹7,499) allows 100 prompts.

Depra AI is AI visibility tracking software for brands in India. It checks how ChatGPT, Gemini, Perplexity and Google AI Overviews mention your brand, in English and Hinglish, from ₹1,999 a month. Based in India, working worldwide.

How to Track Brand Mentions in ChatGPT and AI Answers

ifham baig — Thu, 01 Oct 2026 09:03:16 +0000

Originally published on depra.ai on July 21, 2026 (updated September 15, 2026).

A practical, spreadsheet-based method for monitoring brand mentions across ChatGPT, Gemini, Perplexity and Google AI Overviews. You create 10-20 buying questions that do not name your brand, ask them multiple times per engine, and log the results in a structured sheet.

The 8-column tracking sheet

Column	Purpose	Example
Date	When the question was asked	15 Sep 2026
Engine	Platform used	ChatGPT
Language	English or Hinglish	Hinglish
Prompt	The exact question	India mein oily skin ke liye...
Named	Yes / No / No Overview	Yes
Position	Brand ranking in the response	2
Sentiment	Positive, neutral or negative, with a quote	Neutral: "YourBrand is a budget pick"
Cited sites	Source domains	Three domains

What counts as a brand mention

Variants count: brand name variations, misspellings, domain names and product lines.
Skip look-alikes: words that match your name in ordinary usage do not count.
Position by first appearance: number brands in the order they appear.
Sentiment by sentence: judge tone from the specific sentence that mentions you.
Citations are separate: track source links independently from mentions.
"No Overview" is a valid result: record it when Google does not show an AI Overview.

Setup: half a day spread over 3 days

Day 1, question writing

Write 10-20 questions that buyers typically ask.
Take the wording from reviews, support chats, Q&A sections and sales calls.
Leave your brand name out, or results will be inflated.

Day 1, Hinglish versions

Write Hinglish versions of your 5 most important questions.
Use English letters with everyday Hindi words mixed in.
Pair English and Hinglish versions with matching row IDs.

Day 2, running the questions

Ask each question 3 times per chat engine, each in a separate new chat.
Use private windows for Google searches.
Log out or disable memory between runs.
15 questions x 3 engines x 3 runs is roughly 2 hours of work.

Day 3, roll-up

Apply formulas to calculate mention share per engine.
Work out average position and the most-cited sources.
That gives you a baseline.

Ongoing

Re-run the same questions weekly or monthly, on the same weekday.
Keep old row IDs when questions are unchanged.
Start a new trend line when you reword a prompt.

Why multiple answers matter

AI answers vary a lot from run to run. In Depra's data, two ChatGPT answers to the same question, asked within one hour, shared only 54.7% of the brands they named. Two Perplexity answers shared 68.3%.

Because of that, a single run can show a false positive or a false negative. Asking 15 questions 3 times gives you 45 answers per engine per round, which smooths out much of the randomness.

Tracking brand performance over time

Freeze your question set.
Ask on a fixed schedule.
Compare mention-share percentages between periods.
Always report the sample size next to the percentage.

95% confidence ranges

The range shows how much variation happens by chance alone.

Answer count	Mention share	95% range
20	50%	30% to 70%
50	50%	37% to 63%
100	50%	40% to 60%
135	50%	42% to 58%
400	50%	45% to 55%

Comparison rules

Keep questions, engines and runs consistent across periods.
Compare month to month, or in 4-week intervals.
Count a change as real only when the confidence ranges do not overlap.
Track competitors at the same time to spot platform-wide trends.
Keep a Notes tab recording what changed and when.

Roll-up formulas

These work in Google Sheets and Excel.

Mention share on ChatGPT

=COUNTIFS(B:B, "ChatGPT", E:E, "Yes") / COUNTIF(B:B, "ChatGPT")

Mention share in AI Overviews

=COUNTIFS(B:B, "Google AI Overview", E:E, "Yes") / (COUNTIF(B:B, "Google AI Overview") - COUNTIFS(B:B, "Google AI Overview", E:E, "No Overview"))

Average position on ChatGPT

=AVERAGEIFS(F:F, B:B, "ChatGPT", E:E, "Yes")

Hinglish share on Perplexity

=COUNTIFS(B:B, "Perplexity", C:C, "Hinglish", E:E, "Yes") / COUNTIFS(B:B, "Perplexity", C:C, "Hinglish")

Share of voice: your mention count divided by the total mentions of all tracked brands.

Tracking English vs Hinglish

Give English and Hinglish versions matching row IDs.
Report the two languages side by side rather than blended.
Expect big differences between engines.

Findings from the Depra AI study run on 14 August 2026 (full write-up):

ChatGPT: 2.2 percentage point difference in brand overlap between English and Hinglish.
Gemini: a 7.8 point gap, and sources were cited in 90% of English answers against 41.3% of Hinglish answers.
Perplexity: a 23.4 point gap.

Writing Hinglish prompts well

Use English letters and everyday vocabulary.
Example: "India mein oily skin ke liye 500 rupees ke andar konsa face wash kharidna chahiye?"
Avoid word-for-word Hindi translations.
Have native speakers check each question pair.

When to switch to automated tools

Manual tracking stops being practical when:

Filling the sheet takes longer than acting on the results.
Daily tracking of 20 questions across 3 engines means 1,800+ answers a month.
You keep skipping runs.
Several people score answers differently.
You need daily reporting.

About Depra AI

Depra AI automates this process:

Tracks ChatGPT (with web search), Gemini and Google AI Overviews daily, and Perplexity weekly.
Runs from the countries you choose, in English and Hinglish.
Records mentions, position, sentiment with the quoted sentence, and cited pages.
Includes competitor tracking.
Excludes questions that contain your brand name by default.
Exports data as CSV.

The Starter plan is ₹1,999/month plus GST and includes 16 prompt slots (8 English + 8 Hinglish questions) with up to 1,568 scheduled checks a month. There is a 7-day free trial.

Advantages of the spreadsheet approach

No cost beyond your time.
You can track any engine, including Claude and Microsoft Copilot.
You can test logged-in answers with personal memory switched on.
You have full transparency and control.

FAQ

How do I track brand mentions in ChatGPT for free?
Use a spreadsheet with 10-20 buying questions. Ask each one 3 times in separate chats, log whether you are named plus position, tone and sources, calculate mention share, and repeat monthly.

How many times should I ask each question?
At least 3 times per engine per round, in new chats, then pool the results across questions. At a 50% share, 20 answers give a 30-70% range and 100 answers give a 40-60% range.

What is AI brand monitoring?
Checking how ChatGPT, Gemini, Perplexity and similar engines mention your brand when users ask category questions: how often, in what competitive context, with what tone and from which sources. Also called LLM brand monitoring.

Should I track questions that include my brand name?
Keep them separate. Questions that name your brand almost always get a mention, which inflates share. Use them to see how AI describes you, and report share from unbranded questions.

Can I use Google Sheets to track this?
Yes. The 8-column format and formulas work in Google Sheets and Excel. Depra has no direct Sheets integration, but data exports as CSV.

Which engines should I track in India?
Start with ChatGPT, Gemini, Perplexity and Google AI Overviews, in English and Hinglish. Perplexity and Gemini changed brand recommendations the most between Hinglish and English in our study.

What is a good mention rate?
There is no standard benchmark. Compare your trend over time and against competitors on identical questions.

Does Depra do this automatically?
Yes. Depra runs buying questions daily on ChatGPT with web search, Gemini and Google AI Overviews, and weekly on Perplexity, from your chosen country in English and Hinglish. It stores full answers with scoring for you and your competitors.

ChatGPT SEO: How to Get ChatGPT to Recommend Your Brand

ifham baig — Thu, 01 Oct 2026 08:59:19 +0000

Originally published on depra.ai on 21 Jul 2026, updated 15 Sept 2026.

What OpenAI documents about ChatGPT search, five steps to get your brand named in its answers, and how to tell if the work paid off.

SEO for ChatGPT is usually called GEO (generative engine optimization) or AEO (answer engine optimization): the work of getting your brand named inside AI answers. You get ChatGPT to recommend your brand by letting OpenAI's search crawler read your site, stating your facts the same way everywhere, getting named on the pages ChatGPT cites for your category, and then checking many answers to see if it worked.

What moves you is the evidence ChatGPT finds when it searches: your own pages, and what other websites say about you.

Our own numbers come from 480 AI shopping answers we collected in India on 14 Aug 2026.

What is ChatGPT SEO?

ChatGPT SEO is the work of making ChatGPT name your brand, describe it correctly and link to your site when people ask about your category. Many Google SEO basics still apply: pages a crawler can read, clear answers, and mentions on other trusted sites. The table shows where the two differ.

ChatGPT SEO, SEO for ChatGPT, LLM SEO, GEO and AEO all name the same job. What is GEO and LLM SEO define the terms.

Google SEO compared with ChatGPT SEO

Question	Google SEO	ChatGPT SEO
What you want	A high position in the list of links	Your brand named in the answer, described correctly
Crawler that must reach you	Googlebot	OAI-SearchBot, for ChatGPT search
What a win looks like	Rankings and clicks	The share of answers that name you, and where you come in them
How to check	One search shows a fairly stable ranking	Many answers, because the same question returns different brands

Crawler facts from OpenAI's crawler documentation, checked 15 Sep 2026.

How does ChatGPT search pick its sources?

OpenAI documents the inputs to ChatGPT search. It does not publish how it ranks pages or chooses which ones to cite. Here is what its public pages say, checked on 15 Sep 2026.

It can decide to search. OpenAI's help page on searching the web with ChatGPT says ChatGPT can search the web on its own when a question needs current information.
It rewrites your question. The same page says ChatGPT often turns your question into one or more shorter search queries and sends them to third-party search providers. It may pass on your general location, based on your IP address, and use saved memories if memory is on.
Your site must allow OAI-SearchBot. OAI-SearchBot is the bot OpenAI uses to find pages for ChatGPT search. OpenAI's crawler documentation says sites that block it will not be shown in ChatGPT search answers, though they can still appear as navigational links.
GPTBot is a different bot. GPTBot collects pages that may be used to train OpenAI's models. Blocking it opts your content out of training. OAI-SearchBot has its own robots.txt rule. Our AI crawlers list gives the exact lines for every OpenAI bot.
Any public website can appear. OpenAI's publishers and developers FAQ says any public website can appear in ChatGPT search, and tells site owners to make sure they are not blocking OAI-SearchBot.

What OpenAI does not publish

There is no public ranking formula, no list of trusted websites and no rule for how many brands an answer names. You can see the source links. In our study, ChatGPT with web search showed them in all 80 English answers, 2.5 links per answer on average, so each page it cites for your category carries a lot of weight.

Step 1: Let ChatGPT read your site

Start by checking that OAI-SearchBot can fetch your pages. Open yourdomain.com/robots.txt, the plain text file that tells bots which pages they may fetch, and look for a Disallow rule under User-agent: OAI-SearchBot or under User-agent: *. If a rule blocks the bot, ChatGPT search will not show your pages in its answers.

Then check what the bot can see. Vercel's December 2024 analysis of AI crawlers found that OpenAI's crawlers fetched JavaScript files and did not run them. Prices, sizes, ingredients and delivery times that only appear after a script loads may be invisible to them. Put those facts in the page HTML.

Write each important page so the answer comes first. Use the buyer's question as a heading, answer it in the first two sentences, then add detail. Schema markup, the structured data that labels a page as a product or an FAQ, helps Google. OpenAI does not say whether ChatGPT reads it, so add it for Google and keep every fact in visible text as well.

Adding citations, quotations and statistics raised a page's visibility in AI answers by up to about 40% in a GPT-3.5 test setup. (Aggarwal et al., KDD 2024)

Step 2: Keep your brand facts the same everywhere

When ChatGPT searches, it can build its description of you from several pages. When your site, marketplace listings and directory profiles disagree, the answer can repeat the wrong version or drop the detail. Pick one version of each fact and use it everywhere you control.

One description. Write one sentence that says what you sell and who it is for. Use the same sentence on your homepage, About page, LinkedIn, Google Business Profile and marketplace seller pages.
One product name. Spell each product the same way on your store and on every marketplace.
One set of numbers. Keep prices, pack sizes, certifications and founding year identical. When a price changes, update every listing in the same week.

Step 3: Get named on the pages ChatGPT cites

When ChatGPT searches, it tends to name the brands its source pages name. The quickest route is to find the exact pages ChatGPT cites when it answers your buyers' questions, then work to get your brand onto those pages. How to get cited by AI covers this in depth.

To find the pages, ask ChatGPT your top buying questions with search on and open every source link. Note the ones that name your competitors and leave you out. Those are your targets:

Best-of lists. Articles such as "best face wash for oily skin in India". Write to the author with facts they can check: price, what makes the product different, and a sample if you sell a physical product.
Marketplace listings. Complete your listings on the marketplaces your buyers use and ask real customers for reviews.
Forums and communities. Reddit, Quora and category forums appear as sources in some answers. Take part from a named brand account and answer questions honestly. Hidden promotion breaks the rules of most communities.
Your own comparison pages. Publish an honest comparison of your product against your main rivals, including who should pick the other option.

Doing the lookup at scale

Checking sources by hand works for a handful of questions. Depra's citation gap analysis does it across ChatGPT, Gemini, Perplexity and Google AI Overviews: it lists websites that recommend your competitors but never mention you, with competitors' own sites filtered out.

Step 4: Answer the questions buyers ask ChatGPT, in their words

Buyers rarely type your brand name. They ask for help choosing: which face wash for oily skin under ₹500, which running shoes under ₹3,000. Collect 20 to 40 of these questions from reviews, support chats, sales calls and marketplace Q&A. Then make sure your site answers each one plainly, with the price, who the product suits and who it does not suit.

In India, many buyers type in Hinglish, which is Hindi and English written in English letters. Here is one question from our study in both versions:

English: Which face wash should I buy for oily skin in India under ₹500?
Hinglish: India mein oily skin ke liye 500 rupees ke andar konsa face wash kharidna chahiye?

What changed when buyers asked in Hinglish?

We asked 10 questions like this in both languages, 8 times each, on ChatGPT, Gemini and Perplexity from inside India. ChatGPT answered all 80 Hinglish questions in Hinglish. We compared the share of brands two answers have in common. On ChatGPT, that share was 2.2 percentage points lower for an English and a Hinglish answer than for two repeat answers in the same language. The gap was 7.8 points on Gemini and 23.4 points on Perplexity. Gemini also attached sources to 90.0% of English answers and only 41.3% of Hinglish answers. The full data is in our English vs Hinglish study.

On ChatGPT, the language of the question made little difference. On Gemini and Perplexity it did, so track the Hinglish versions of your questions too. Hinglish AI visibility shows how Depra tracks both languages.

Step 5: Check many answers to see if it worked

Measure with many answers, because ChatGPT rarely gives the same answer twice. In our study, two ChatGPT answers to the same question, asked within one hour from India, shared 54.7% of the brands they named. One screenshot can make you look like the winner or like you are missing. Does ChatGPT give the same answer to everyone? has the full breakdown.

Count the share of answers that name you. Ask each question several times in a new chat, logged out or with memory off, and add up the results across your questions. For a brand named in half of its answers, 20 answers give a 95% confidence range of about 30% to 70%, and 100 answers narrow it to about 40% to 60%. A confidence range is the band the true share most likely sits in. How to measure AI visibility explains the maths, and how to track brand mentions in AI answers gives you a spreadsheet to copy.

Freeze the questions. Keep the same wording each month, so a change in results comes from ChatGPT and your work on the web.
Log more than the mention. Record your position, the tone, the sentence that names you and every cited site. A wrong price in an answer points to the page you need to fix.
Compare periods. Put this month's share next to last month's. Treat a change as real only when the two confidence ranges do not overlap.

What does a ChatGPT SEO tool do?

A ChatGPT SEO tool asks your buyers' questions to ChatGPT on a schedule, stores the answers and counts how often each brand is named. It replaces the manual checking in Step 5. The work in Steps 1 to 4 still happens on your site and on other websites.

What a tool can do

Ask the same questions every day from your country, with web search on, and keep every answer.
Show the share of answers that name you and each competitor, your position and the tone.
List the websites cited in answers, and which of them name competitors and leave you out.
Check whether your robots.txt blocks OAI-SearchBot and other AI crawlers.

What a tool cannot do

Change what ChatGPT says, or promise a recommendation.
Show the exact answer each person gets. Memory, chat history and location make individual answers differ.
Prove why ChatGPT chose a brand. Cited sources show where to look first.
Give a reliable count of how often a question is asked. OpenAI does not publish that number.

How Depra does it

Depra's ChatGPT visibility tracker asks your questions to ChatGPT with web search every day from inside your chosen country, next to Gemini and Google AI Overviews daily and Perplexity weekly. It suggests buying questions that do not name your brand, with Hinglish versions when India is a market, stores every answer, and scores you against your competitors. Plans start at ₹1,999 a month plus GST, with 7 days free on any plan, card required. See Depra pricing.

A worked example: a ₹449 face wash brand in India

A made-up D2C brand sells a ₹449 face wash for oily skin. The example shows the order of work, with no promised result: OpenAI publishes no timeline for how quickly answers change.

Week 1, check access. An old robots.txt rule blocks every bot except Googlebot. The team allows OAI-SearchBot and moves the price and skin type from an image carousel into the page text.
Week 1, fix facts. The product is called Oil Control Face Wash on the site and Oil Free Cleanser on a marketplace. They pick one name and update every listing.
Week 2, answer the question. They publish a page that answers the under ₹500 question honestly, including which skin types should choose a different product.
Weeks 2 to 6, get named. They ask ChatGPT the question 5 times with search on and open every source. Two best-of articles name competitors, so they send both authors the price, ingredient list and samples.
Every month, measure. They ask 15 buying questions 5 times each, in English and Hinglish, and compare their share with the month before, ranges included.

Frequently asked

What is SEO for ChatGPT called?

It is usually called GEO, short for generative engine optimization, or AEO, short for answer engine optimization. Some people say LLM SEO or ChatGPT SEO. All four describe the work of getting your brand named and cited in AI answers. AEO vs GEO vs SEO compares the terms.

How do you rank in ChatGPT?

ChatGPT has no fixed ranking to climb. Each answer names a few brands, and the list changes between answers. To show up more often, allow OAI-SearchBot in robots.txt, put clear answers and facts in your page text, and get named on the websites ChatGPT cites for your category.

How do I show up in ChatGPT search results?

First make sure your robots.txt does not block OAI-SearchBot. OpenAI's crawler documentation says blocked sites will not be shown in ChatGPT search answers. After that, work on getting named on the pages ChatGPT already cites for your category.

Can I pay ChatGPT to recommend my brand?

No. OpenAI has started showing ads in ChatGPT, labelled as ads and kept separate from the answer, and its help page on ads in ChatGPT says ads do not influence ChatGPT's answers. A recommendation inside the answer comes from what ChatGPT finds about you.

Does blocking GPTBot remove my site from ChatGPT?

No. GPTBot collects pages that may be used to train OpenAI's models. ChatGPT search uses OAI-SearchBot, which has its own robots.txt rule, so allow OAI-SearchBot whatever you decide about GPTBot. Our AI crawlers list has the lines to copy.

How long does ChatGPT SEO take to work?

OpenAI says a robots.txt change takes about 24 hours to reach its systems. For everything else it publishes no timeline. A new mention on a page ChatGPT cites can show up the next time ChatGPT searches, while anything the model learned in training changes only with a new model. Measure monthly and compare periods.

What if ChatGPT recommends my brand but gets the facts wrong?

Open the source links in that answer and find where the wrong fact lives: your own site, a marketplace listing, a directory or an old article. Correct it there, then ask the same question again over the following weeks. ChatGPT often repeats what its sources say.

See what ChatGPT says about your brand

Depra asks your buyers' questions to ChatGPT with web search, Gemini, Google AI Overviews and Perplexity, and shows which brands get named. Start 7 days free on any plan, card required. See how it works.

Keep reading

Why Your Brand Is on Perplexity but Not ChatGPT

ifham baig — Thu, 01 Oct 2026 08:51:53 +0000

Originally published on depra.ai on 15 Sept 2026.

Each AI engine runs its own search and cites its own websites, so one buying question can name your brand on Perplexity and skip it on ChatGPT.

Your brand can appear on Perplexity but not ChatGPT because each engine runs its own web search, reads a different set of websites and writes its own shortlist of brands. In our study of 480 AI answers to buying questions asked from inside India, ChatGPT and Perplexity shared only 32% of the brands they named for the same question, while two ChatGPT answers to that same question shared 55%.

Shared brands means the brands two answers have in common, divided by all brands either answer named. A gap between engines is normal, and you can measure it. Below: how each engine builds an answer, what our data shows, and a checklist of causes to test.

Why does my brand appear on Perplexity but not ChatGPT?

The two engines read very different parts of the web before they answer. A citation is a link to a source page that an engine shows next to its answer, and citations are the visible trace of where an answer came from.

In English, Perplexity showed 19.6 citations per answer on average. ChatGPT, with web search on, showed 2.5. We asked every question in English and in Hinglish, which is Hindi-English typed in English letters ("India mein oily skin ke liye 500 rupees ke andar konsa face wash kharidna chahiye?"). Across 160 answers each, Perplexity cited 167 different websites and ChatGPT cited 76.

The mix of websites differs too:

Perplexity cited reddit.com in 101 of its 160 answers. ChatGPT cited it in 3.
Perplexity cited youtube.com in 88 answers. ChatGPT cited it in 2.
Perplexity cited amazon.in in 79 answers and flipkart.com in 70. ChatGPT cited amazon.in in none and flipkart.com in 4.

When we matched the two engines on the same question in the same language, ChatGPT had cited only 12% of the websites Perplexity used (43 of 358 question and website pairs).

The brand lists split just as sharply. For each question we pooled the 8 runs per engine and language. Perplexity named 163 brand and question combinations at least once. 70 of them (43%) never appeared in any of ChatGPT's 8 answers to the same question in the same language. The gap runs the other way as well: 64 of ChatGPT's 157 combinations (41%) never appeared on Perplexity.

If buyers discuss your brand mostly in Reddit threads, YouTube reviews and marketplace listings, Perplexity's source mix gives it more ways to find you. ChatGPT cites far fewer pages per answer, so a handful of articles and brand sites shape its shortlist. Citations show what an engine displayed. They do not prove why it named a brand, so use them as the first place to look.

How each AI engine builds an answer

ChatGPT. OpenAI's help page on searching the web with ChatGPT says ChatGPT may search the web on its own when a question needs current information. It often rewrites your question into one or more shorter search queries, sends them to search partners, and can use an approximate location based on your IP address. If memory is on, saved memories can shape those queries too. The same page says a site must allow OAI-SearchBot, the bot OpenAI uses to fetch pages for search, to be eligible for ChatGPT search results. OpenAI's crawler documentation adds that sites which disallow it "will not be shown in ChatGPT search answers". GPTBot, OpenAI's training crawler, is a separate setting.

Perplexity. Perplexity's help page on how Perplexity works says it searches the internet in real time, summarises what it finds, and adds citations that link to the original sources. Its crawler guide lists two bots. PerplexityBot surfaces and links websites in Perplexity search results. Perplexity-User visits pages when a person asks a question, and the guide says it generally ignores robots.txt, the file that tells bots which pages they may fetch. In our data Perplexity was the most source-heavy engine and also the most repeatable: two runs of the same question shared 68% of their brands and 85% of their cited websites.

Gemini. Gemini is Google's AI assistant at gemini.google.com. Google's developer guide to grounding with Google Search says a Gemini model first decides whether a Google Search would improve the answer, then writes one or more search queries, runs them and builds its reply from the results with links to its sources. That guide covers the Gemini API. Google's help page on sources in Gemini Apps says the app may show links to sources, including public websites, below or within a response. In our captures it attached sources to 72 of 80 English answers, with 9.4 citations per answer, and wrote the longest English answers (4,609 characters on average). Its brand lists sat closer to ChatGPT's (38% shared) than to Perplexity's (33%).

Google AI Overviews. An AI Overview is the AI-written summary Google shows above some search results. Google's guide to AI features in Search says a page must be indexed and eligible to show in Google Search with a snippet to appear as a supporting link, with no extra technical requirements. Our study did not include AI Overviews, so this post has no comparison figures for them.

What our study shows about engine differences

The figures below come from 480 answers: 10 buying questions (5 skincare, 5 fashion), each in English and Hinglish, asked 8 times to each engine from inside India on 14 Aug 2026. "When asked again" compares repeat runs of the same question on one engine. Engine pairs compare runs of the same question in the same language.

Measure	ChatGPT	Gemini	Perplexity
Citations per answer, English	2.5	9.4	19.6
Citations per answer, Hinglish	2.6	5.2	18.8
Different websites cited, 160 answers	76	97	167
Brands named per answer	4.3	5.5	5.3
Brands shared when asked again	55%	51%	68%
Same first brand when asked again	60%	63%	69%
Extra brand change, English vs Hinglish	2.2 points	7.8 points	23.4 points

The last row shows how much less brand lists matched between English and Hinglish versions of a question than between two identical runs.

Engine pair	Brands shared, same question	Same first brand
ChatGPT and Gemini	38%	39%
ChatGPT and Perplexity	32%	18%
Gemini and Perplexity	33%	15%

What the two tables show:

Every pair of engines agreed less than any single engine agreed with itself. The lowest self-match was Gemini at 51%. The highest match between two engines was 38%.
ChatGPT and Perplexity picked the same first brand in 18% of answer pairs. Each engine repeated its own first brand in 60% to 69% of reruns. Being named first on one engine tells you little about the other.
Sources barely overlapped across engines. Two answers to the same question from different engines had 4% to 5% of their cited websites in common, against 33% to 85% for reruns on one engine.

Single brands show the split clearly. These are English answers that named the brand, out of 80 per engine:

Brand	ChatGPT	Gemini	Perplexity
Plum	9	15	23
Taneira	0	1	12
La Roche-Posay	0	0	11
The Derma Co	20	30	2
Minimalist	29	36	18

Taneira and La Roche-Posay match the question in this post's title: regular on Perplexity, absent from ChatGPT. The Derma Co shows the reverse, named in 20 ChatGPT answers and 2 Perplexity answers. The full study is on the English vs Hinglish AI shopping study page.

Method and limits

The answers come from Depra's English vs Hinglish study, collected on 14 Aug 2026 in one 57-minute window. ChatGPT answers were captured from the chatgpt.com web app with search on, Gemini answers from the gemini.google.com web app, and Perplexity answers from Sonar, the answer model Perplexity sells through its API, with live web search. All requests were logged out and located in India. The rerun, language and citations-per-answer figures are from the published study. The engine-pair figures, pooled brand gaps, website counts and website overlap are our re-analysis of the same 480 stored answers, run on 15 Sep 2026 with the study's fixed brand list and matching rules. Limits: 10 questions in two categories, one country, one day, and no logged-in history or memory. Brand counts cover all 10 questions, so a skincare brand could only be named in the 40 skincare answers. Brands missing from the study's brand list were not counted. Citations are the links engines displayed, which do not prove why a brand was named. Depra sells an AI visibility tracker, so read the numbers with that interest in mind.

Why one AI model recommends your brand and another does not

You can test each cause below yourself.

The engine's crawler is blocked. If your robots.txt blocks OAI-SearchBot, OpenAI says your pages will not show in ChatGPT search answers, even when PerplexityBot is allowed. Check each bot by name. Check your firewall and CDN (the service that delivers your site) too: OpenAI's help page asks sites to allow traffic from its published search bot IP addresses.
The engine cites websites that leave you out. Open 5 to 10 answers on each engine and list the cited pages. If Perplexity cites Reddit threads and marketplace listings where you appear, and ChatGPT cites two roundup articles that skip you, those two articles are your target.
The question's language. In our study, Perplexity's brand lists matched 23.4 points less between English and Hinglish versions of a question than between identical runs. ChatGPT's matched 2.2 points less. A brand that shoppers discuss in Hinglish can appear for Hinglish questions on one engine and not the other.
Location. ChatGPT can use an approximate location from your IP address, and a VPN can change it. Our Perplexity requests set India as the search country. A check run from India and one run through a foreign VPN can return different brands.
One check is not a result. Each engine changes its own answer between runs. Two ChatGPT answers to the identical question shared 55% of their brands. A single check where you are missing on ChatGPT and present on Perplexity can flip on the next try, so ask each question several times before you decide anything.
Personal context. OpenAI's help page says saved memories can shape ChatGPT's search queries. Test logged out, or with memory turned off, so your own history does not tilt the result.

How to find out which engine is the problem

You can run the test by hand. Pick 10 buying questions that do not contain your brand name. Ask each one 5 times on ChatGPT with web search, on Perplexity and on Gemini, logged out. Record the brands named, your position in the list and the cited links. That is 150 answers per round. If you are missing on one engine across most runs and present on another, compare that engine's cited websites first. Our guide to tracking brand mentions in AI answers covers the sheet setup.

Depra automates this check. It asks your buyers' questions to ChatGPT (the web app with search on), Gemini and Google AI Overviews daily, and to Perplexity (Sonar with live web search) weekly, all from inside your country. Every full answer is stored. You see how often each engine names you, which websites each engine cited, and the websites that recommend your competitors but never mention you. Questions that already contain your name are not counted. Each engine has its own page: the ChatGPT visibility tracker, the Perplexity visibility tracker and the Gemini visibility tracker. The AI crawler checker shows which of 41 AI crawlers your robots.txt allows or blocks, with the exact rule for each bot. If ChatGPT is the engine that leaves you out, the guide to why ChatGPT doesn't mention your brand goes deeper.

See which engines name your brand and which skip it. Start 7 days free on any plan, no card.

Frequently asked questions

Why does my brand appear on Perplexity but not ChatGPT?

Perplexity and ChatGPT run separate web searches and cite different websites. In our study of 480 answers to buying questions asked from inside India, Perplexity showed 19.6 citations per English answer and ChatGPT showed 2.5, and ChatGPT cited only 12% of the websites Perplexity used for the same questions. If the few pages ChatGPT relies on leave you out, or your robots.txt blocks OAI-SearchBot, you can be named on Perplexity and missing on ChatGPT.

Why does one AI model recommend my brand while another doesn't?

Each engine runs its own search, reads its own sources and builds its own shortlist, so engines agree with each other less than they agree with themselves. In our study, two engines shared 32% to 38% of the brands named for the same question, while two runs on the same engine shared 51% to 68%. Language, location, crawler access and normal run-to-run change all widen the gap.

Why isn't my brand mentioned in ChatGPT and Perplexity answers?

When both engines leave you out, look for causes they share: the websites both cite for your category do not mention you, your robots.txt blocks OAI-SearchBot or PerplexityBot, or your brand is missing from the forums, review sites and marketplaces that answers in your category cite. Check crawler access first, then read the pages each engine cites for your top buying questions and work on getting into them.

Do ChatGPT and Perplexity recommend the same brands?

Only in part. Across 10 buying questions asked 8 times each in English and Hinglish, ChatGPT and Perplexity shared 32% of the brands they named for the same question and picked the same first brand in 18% of answer pairs. La Roche-Posay appeared in 11 of 80 English Perplexity answers and in none of ChatGPT's.

Does ChatGPT Give the Same Answer to Everyone?

ifham baig — Thu, 01 Oct 2026 08:47:56 +0000

Originally published on depra.ai on 15 Sept 2026.

We asked ChatGPT, Gemini and Perplexity the same buying questions over and over, then asked ChatGPT again a month later, and measured how much the brand lists changed.

No, ChatGPT does not give the same answer to everyone, and it rarely gives the same answer twice even to one person. We asked ChatGPT 10 buying questions, in English and Hinglish, 8 times each from inside India within one hour, which gave 160 answers. On average, two answers to the same question shared only 54.7% of the brands they named.

That was with no login, no chat history and one country. Real users add more variation: OpenAI says ChatGPT can use your memories and your approximate location, so two people asking the same thing start from different places. Below are our numbers for ChatGPT, Gemini and Perplexity, a fresh ChatGPT rerun from 15 Sep 2026, and what the variation means if you check your brand by hand.

What we measured

The data comes from Depra's English vs Hinglish study. We wrote 10 buying questions, 5 about skincare and 5 about fashion, such as "Which face wash should I buy for oily skin in India under ₹500?". Each question had an English version and a Hinglish version, which is Hindi-English typed in English letters. We asked each version 8 times to three engines on 14 Aug 2026, in one 57-minute window, located in India, with no account logged in. That gave 480 answers.

ChatGPT answers were captured from the chatgpt.com web app with web search on. Gemini answers came from the gemini.google.com web app. Perplexity answers came from Sonar, the answer model Perplexity sells through its API, with live web search.

To compare two answers, we listed the brands each one named and worked out the overlap: brands both answers named, divided by all brands either answer named. Here are two ChatGPT answers to the face wash question, asked 10 minutes apart:

Answer 7: Minimalist, The Derma Co, Dot & Key
Answer 8: Minimalist, The Derma Co, Cetaphil

They share 2 brands out of 4 different brands named, so the overlap is 50%. Identical lists score 100%. Lists with nothing in common score 0%. With 8 answers per question, language and engine, there are 28 pairs of repeat answers each time, or 560 pairs per engine across the study.

How different are ChatGPT, Gemini and Perplexity answers to the same question?

Every row compares repeat answers to the identical question, on the same engine, in the same language.

Repeat answers to the same question	ChatGPT	Gemini	Perplexity
Average brand overlap	54.7%	51.2%	68.3%
Exactly the same brands	55 of 560 (9.8%)	27 of 560 (4.8%)	86 of 560 (15.4%)
Same brands in the same order	29 of 560 (5.2%)	5 of 560 (0.9%)	34 of 560 (6.1%)
Same brand named first	60.0%	63.2%	69.5%
Average overlap of cited websites	33.4%	33.1%	85.2%

What the table shows:

ChatGPT repeated its exact brand list in about 1 pair of answers in 10, and its exact order in about 1 in 20.
On average, two ChatGPT answers to the same question shared only a third of their cited websites. Each run read a different set of pages.
Perplexity was the most repeatable engine, with 68.3% average brand overlap and 85.2% average cited-website overlap between reruns.

The face wash question shows the pattern up close. Across 8 English answers, ChatGPT named 10 different brands, 3 to 5 per answer. The Derma Co appeared in all 8. Minimalist appeared in 6, Simple in 5, Dot & Key and Chemist At Play in 3 each, Plum and Fixderma in 2, and Saslic, Deconstruct and Cetaphil once each.

Across the whole study, ChatGPT produced 157 brand and question pairings, meaning a brand named at least once for a given question in a given language. 37 of them appeared in all 8 answers. 29 appeared once and never again. The top of each list is fairly stable. The rest rotates.

Other research points the same way. SparkToro and Gumshoe ran 12 brand recommendation prompts 60 to 100 times per platform with volunteers. As Search Engine Journal reported in January 2026, ChatGPT and Google's AI answers returned the same brand list less than 1 time in 100, and the same list in the same order less than 1 time in 1,000. The original write-up is on SparkToro's blog. Our exact-list rates for ChatGPT (9.8%) and Gemini (4.8%) are higher. Our answers were collected within one hour, from one country, with no login.

Does ChatGPT give the same answer a month later?

The study covered one hour. On 15 Sep 2026, a month later, we asked ChatGPT the same 10 English questions 3 more times each: from India, logged out, with web search on. That gave 30 new answers.

On average, two August answers to the same question shared 54.1% of their brands (280 pairs, English only).
On average, a September answer and an August answer shared 50.1% (240 pairs).
On average, two September answers shared 46.4% (30 pairs).

A month apart, answers differed only a little more than answers asked minutes apart. The core brands held: brands ChatGPT named in all 8 August answers to a question (19 English brand and question pairings) appeared in 53 of the 57 matching September answers. New names did turn up. One September answer to the vitamin C serum question recommended Cureskin, a brand that appeared in none of the 480 August answers. Three runs per question is a small sample, so read this as a spot check.

Method and limits

The August overlap, first-brand and cited-website figures come from the published study report, summarised on the English vs Hinglish AI shopping study page. The exact-list and same-order counts, the brand pairing counts and the face wash breakdown are our re-analysis of the same stored answers on 15 Sep 2026. Brands were counted with the study's fixed brand list and matching rules, so a brand missing from that list was not counted. For the September comparison we added 9 brand names that appeared in the new answers and were missing from the list, Cureskin and U.S. Polo Assn. among them, and re-counted both months with the longer list. That is why the August English figure there is 54.1%. Limits: 10 questions in two categories, one country, logged-out sessions only, and 3 September runs per question. Nothing here measures logged-in users with memory or chat history. Depra sells an AI visibility tracker, so weigh the numbers with that in mind. This post replaces an earlier Depra post on the same topic that misstated a confidence range; the corrected example is in the section on checking your brand once.

Why do AI answers change each time?

Three causes, each documented in public sources or visible in our data:

Randomness in the writing. A language model writes an answer one word at a time and picks each next word with some randomness. OpenAI's API reference describes a temperature setting: higher values make output more random, lower values make it more focused. OpenAI's help pages do not state the setting the ChatGPT app uses.
Live search. With web search on, ChatGPT rewrites your question into one or more search queries and sends them to search partners, according to OpenAI's help page on searching the web with ChatGPT. Search results shift, so the pages behind each answer shift too.
Answer length. Some answers give one pick, others a long list. In August, ChatGPT's 8 answers to the running shoes question named anywhere from 2 to 7 brands.

A 2026 paper, Don't Measure Once by Schulte, Bleeker and Kaufmann, draws on published studies to reach the same point: AI search answers vary across runs, prompts and time, so one-off checks are unreliable. It calls for repeated measurement and for describing visibility as a spread of outcomes.

Does ChatGPT give different answers to different people?

Our data cannot fully answer this. Every request used the same question, the same country, the same hour and no login. Real people differ on all four, and OpenAI documents several ways that changes the answer:

Memory. OpenAI's Memory FAQ says memory lets ChatGPT remember context from your chats, files and connected apps to personalise responses. Temporary Chats do not use existing memories.
Search queries. OpenAI's search help page says that if memory is on, ChatGPT may use saved memories when it rewrites a search query.
Location. The same page says ChatGPT may estimate your general location from your IP address, and a VPN can change that estimate.
Wording. People rarely type the same words. In SparkToro's study, 142 volunteers wrote their own prompts about headphones and almost no two looked alike, yet Bose, Sony, Sennheiser and Apple still appeared in 55% to 77% of the 994 answers.
Language. In our study, switching a question from English to Hinglish lowered brand overlap beyond normal rerun change by 2.2 points on ChatGPT, 7.8 on Gemini and 23.4 on Perplexity.

Two strangers asking the same thing can see different brands. In our data, one person asking twice usually would.

What this means if you check your brand once

One check is one sample. Minimalist appeared in 6 of ChatGPT's 8 English answers to the face wash question. The other 2 left it out, so 1 check in 4 would have suggested ChatGPT ignores the brand.

The fix is to count, then report a range, the way pollsters report an election: a share, a margin of error and the sample size. For AI answers, the share is how often an engine names you. The range comes from the Wilson score interval, a standard formula for the likely spread of a percentage measured on a small sample. A 95% range is the band the true share most likely sits in.

Answers checked	Brand named in	Share	95% range
8	6	75.0%	40.9% to 92.9%
30	15	50.0%	33.2% to 66.8%
45	28	62.2%	47.6% to 74.9%
100	50	50.0%	40.4% to 59.6%
400	200	50.0%	45.1% to 54.9%

Our earlier post gave the 28 of 45 case as 54% to 70%. That range was too narrow. At 50%, 30 answers leave about 17 points either way, 45 answers about 14, and 100 answers about 10. If your share moves from 55% to 60% on 45 answers, the data cannot tell that apart from noise.

How to get a reliable read

Ask each question several times. 5 runs per question per engine turns a yes or no into a share. More runs narrow the range.
Test logged out or in a Temporary Chat, so your own memory and history do not tilt the result.
Keep everything else fixed: the same questions, wording, country, language and engine each time.
Leave your brand name out of the question. A question that names you will mention you.
Track the share over days, and judge each change against its range.

Our guide to tracking brand mentions in AI answers covers a by-hand sheet, and the guide on how to measure AI visibility covers which numbers to report.

Depra runs this routine for you. It asks your buyers' questions to ChatGPT (the web app with search on), Gemini and Google AI Overviews every day, and to Perplexity every week, from inside your country with no logged-in history. Every full answer is stored, you see how often each engine names you over time, and questions that already contain your name are not counted. Your report shows a confidence range next to the number of answers behind it. See AI visibility tracking, the ChatGPT visibility tracker and how Depra measures AI answers.

See how often ChatGPT names your brand across repeat answers. Start 7 days free on any plan, no card.

Frequently asked questions

Does ChatGPT give the same answer to everyone?

No. ChatGPT rarely gives the same answer twice, even to one person. We asked ChatGPT 10 buying questions, in English and Hinglish, 8 times each from inside India within one hour, with no login (160 answers). On average, two answers to the same question shared 54.7% of the brands they named. Real users add more variation, because OpenAI says ChatGPT can use your memories and your approximate location.

Why does ChatGPT give different answers to the same question?

ChatGPT writes each answer word by word with some randomness, and with web search on it rewrites your question into search queries whose results change. The pages it reads differ from run to run: in our study, two ChatGPT answers to the same question shared only 33.4% of their cited websites on average. Memory, location and wording add further differences between people.

Does ChatGPT recommend the same brands every time?

Only the most-named brands come back reliably. In our study, ChatGPT gave exactly the same brand list in 55 of 560 pairs of repeat answers (9.8%) and the same list in the same order in 29 (5.2%). Counting each brand once per question in a given language, there were 157 brand and question pairings: 37 appeared in all 8 answers and 29 appeared only once.

How many times should I ask ChatGPT to check my brand?

Enough times to count a share. Ask each question at least 5 times, logged out, and add up the answers across your questions. At 50% visibility, 50 answers give a 95% range of about 37% to 63%, and 100 answers narrow it to about 40% to 60%.

Which Websites Does ChatGPT Cite in India?

ifham baig — Thu, 01 Oct 2026 08:40:24 +0000

Originally published on depra.ai on 15 Sept 2026.

The websites ChatGPT, Gemini and Perplexity linked in 480 skincare and fashion answers asked from inside India, and where a brand can get mentioned.

For Indian shopping questions, ChatGPT mostly cites brand websites and Indian news shopping pages, such as beminimalist.co, ndtvshopping.com and hindustantimes.com, and it almost never cites YouTube or Reddit. Across ChatGPT, Gemini and Perplexity together, YouTube was the most cited website with 534 of 4,640 citations, ahead of Reddit (191) and Nykaa (176), and 518 of those YouTube links came from Perplexity.

The numbers come from Depra's Hinglish vs English AI shopping study: 480 answers collected from inside India on 14 August 2026. We asked 10 buying questions (5 skincare, 5 fashion) 8 times each in English and 8 times each in Hinglish, which is Hindi and English mixed and typed in English letters. The engines were ChatGPT with web search, Gemini and Perplexity. A citation is a link an answer shows as its source. The AI search glossary explains the other terms used here.

The 20 websites AI cited most for Indian shopping questions

There are two fair ways to count. By citations, every link counts, so a site linked five times in one answer scores five. By answers, a site scores once for each answer that links it at all. The table ranks by citations and shows both.

#	Website	Citations	Share of 4,640	Answers citing it (of 480)	Type
1	youtube.com	534	11.5%	100	Video platform
2	reddit.com	191	4.1%	106	Forum
3	nykaa.com	176	3.8%	93	Marketplace
4	magicpin.in	133	2.9%	36	Company blog
5	theformalclub.in	117	2.5%	24	Brand blog
6	indisangam.com	113	2.4%	27	Brand blog
7	amazon.in	100	2.2%	79	Marketplace
8	instagram.com	100	2.2%	67	Social media
9	smytten.com	99	2.1%	52	Company blog
10	flipkart.com	90	1.9%	79	Marketplace
11	glowdekho.com	81	1.7%	30	Review blog
12	hindustantimes.com	77	1.7%	48	News publisher
13	thedermaco.com	65	1.4%	36	Brand blog
14	reviehub.in	63	1.4%	32	Review blog
15	blog.flash.co	58	1.2%	43	Company blog
16	idiva.com	55	1.2%	39	Lifestyle publisher
17	medium.com	55	1.2%	7	Blogging platform
18	ndtvshopping.com	53	1.1%	49	News publisher
19	meesho.com	52	1.1%	38	Marketplace
20	deyga.in	50	1.1%	30	Brand blog

How we labelled the type column: we read the cited URLs for each website and labelled it by the kind of page that was cited. Nykaa, Amazon.in, Flipkart and Meesho are marketplaces because their cited pages were category and search listings. The Formal Club, IndiSangam, The Derma Co and Deyga are brands, and their cited pages were posts on their own blogs, such as a shirt brand's list of the top 10 formal shirt brands in India. Of The Formal Club's 117 links, 8 went to its homepage. magicpin.in, smytten.com and blog.flash.co were cited only for blog posts, so they are labelled company blogs. medium.com was cited for a single post, so it is labelled a blogging platform. thedeconstruct.in also had 50 citations and sits just outside the top 20 on the alphabetical tiebreak.

Grouped, these 20 websites hold 2,262 citations, 48.8% of the total:

Video, forum and social platforms (YouTube, Reddit, Instagram): 825
Company blogs, review blogs and Medium (magicpin, Smytten, Glowdekho, Reviehub, Flash, Medium): 489
Marketplaces (Nykaa, Amazon.in, Flipkart, Meesho): 418
Brand blogs (The Formal Club, IndiSangam, The Derma Co, Deyga): 345
News and lifestyle publishers (Hindustan Times, iDiva, NDTV Shopping): 185

1,839 of the 4,640 citations (39.6%) went to list pages, meaning URLs with "best" or "top" in the path. One page can also build a large count. All 113 indisangam.com citations went to a single article on the best kurta set brands, and 109 of theformalclub.in's 117 went to one list post on its blog. Answers often linked the same page several times, each link highlighting a different passage.

Which websites does ChatGPT cite?

ChatGPT showed far fewer links than the other two engines: 408 citations across 160 answers, about 2.5 per answer, spread over 76 websites. Its most cited websites were:

beminimalist.co, Minimalist's own site: 29
ndtvshopping.com: 25
hindustantimes.com: 25
pepejeans.in: 17
cetaphil.in: 16
nykaa.com, dotandkey.com and whowhatwear.com: 14 each

By type, 33.1% of ChatGPT's links went to brand websites, 20.1% to publishers and blogs and 8.3% to marketplaces. Social platforms took 1.2%: YouTube 2 links and Reddit 3, out of 408. The other 37.3% went to websites without a type label. The last section explains how the types were labelled.

Every one of ChatGPT's 408 links carried the tag utm_source=chatgpt.com, so a click from a ChatGPT answer reaches your site labelled with its source. Access matters too. OpenAI's crawler documentation says sites that opt out of OAI-SearchBot, its search crawler, "will not be shown in ChatGPT search answers, though can still appear as navigational links". If ChatGPT never names you, why ChatGPT doesn't mention your brand covers the other common causes.

Does ChatGPT cite different websites than Perplexity and Gemini?

Yes. All three are forms of LLM search: a large language model looks up web pages, then writes an answer that links to some of them. For the same questions, the three engines linked different numbers of sources and different kinds of websites.

Engine	Answers	Citations	Citations per answer (English / Hinglish)	Websites cited	Top 3 websites by citations
ChatGPT (web search on)	160	408	2.5 / 2.6	76	beminimalist.co, ndtvshopping.com, hindustantimes.com
Gemini	160	1,164	9.4 / 5.2	97	indisangam.com, magicpin.in, theformalclub.in
Perplexity	160	3,068	19.6 / 18.8	167	youtube.com, reddit.com, amazon.in

Perplexity produced 66.1% of all citations in the study (3,068 of 4,640), so any all-engine ranking mostly describes Perplexity. That is why YouTube tops the combined list: Perplexity linked it 518 times, Gemini 14 times and ChatGPT twice. Social platforms made up 26.6% of Perplexity's citations, 1.7% of Gemini's and 1.2% of ChatGPT's.

Gemini leaned hardest on list pages. 65.0% of its citations pointed at URLs with "best" or "top" in the path, against 39.5% for ChatGPT and 30.0% for Perplexity. Perplexity's figure is pulled down by YouTube video links, which carry no words in the URL.

The one website all three cited often was Nykaa: 14 citations on ChatGPT, 83 on Gemini and 79 on Perplexity. A single list of "sites AI cites" hides three different answers, so check each engine on its own. In Depra's Perplexity visibility tracker, you can filter the cited websites by engine.

Do sources change when the question is in Hinglish?

Yes, most of all on Gemini and Perplexity. Gemini showed at least one citation in 90.0% of English answers (72 of 80) and in 41.3% of Hinglish answers (33 of 80). Its citations fell from 749 to 415. Marketplace links held steady (73 in English, 79 in Hinglish), so their share nearly doubled, from 9.7% of Gemini's links to 19.0%.

Which websites get cited shifts too. Answers change between reruns of the same question, so the fair test compares the overlap in cited websites between reruns of the same question with the overlap between its English and Hinglish versions. Overlap here is the share of websites two answers have in common out of all websites either one cited. The gap between the two is the language effect.

Engine	Overlap between reruns, same language	Overlap between English and Hinglish	Gap
ChatGPT	33.4%	30.3%	3.1 points
Gemini	33.1%	10.8%	22.3 points
Perplexity	85.2%	52.8%	32.4 points

Some websites nearly vanished in Hinglish. theformalclub.in went from 109 citations in English to 8, and glowdekho.com from 73 to 8. Others grew: magicpin.in went from 53 to 80, and on ChatGPT Minimalist's own site went from 9 to 20. Perplexity cited a Nykaa page in 36.3% of English answers and 47.5% of Hinglish answers. YouTube (250 English, 284 Hinglish) and Reddit (97, 94) held steady.

If your buyers ask in Hinglish, a source list built from English questions is incomplete. The brands named change as well, which our post on how Hinglish changes the brands AI recommends covers.

Why do YouTube and Reddit show up so often?

In this data they show up often on one engine. 97.0% of YouTube citations (518 of 534) and 95.3% of Reddit citations (182 of 191) came from Perplexity.

The YouTube links were concentrated. The 534 citations pointed at 66 distinct videos, linked in 100 answers. Perplexity linked a YouTube video in 56 of its 80 skincare answers and in 32 of its 80 fashion answers.

All 191 Reddit links in these AI search answers came from Indian communities, led by r/IndianSkincareAddicts (82 citations), r/IndianFashionAddicts (42), r/indianrunners (16), r/BuyItForLifeIndia (16), r/skincareaddictsindia (13) and r/SneakersIndia (10).

Our data shows where the links went. It cannot show why an engine chose them. Content deals do not explain the pattern by themselves either. Google announced it had gained access to Reddit's Data API on 22 February 2024, yet Gemini cited Reddit 6 times in 160 answers here, and it cited YouTube, which Google owns, 14 times.

What to do if AI cites sites that never mention your brand

Start with the websites already cited for your buyers' questions, then get your brand onto those pages. Based on this data:

Get into the list pages. 39.6% of citations went to "best" or "top" URLs, many on small Indian review blogs and on other brands' blogs. Find the lists cited for your category and send the authors samples and facts they can check. Publish an honest comparison page on your own site as well: The Formal Club and The Derma Co were cited for list posts on their own blogs.
Fix your marketplace listings. The cited Nykaa, Amazon.in, Flipkart and Meesho pages were category, search and price-band listings, such as Nykaa's face wash under ₹500 page. 82 of the 176 Nykaa links jumped to a highlighted passage, usually a product's name, size and price. If your product sits in the wrong category or price filter, it is missing from the page the answer links.
Get reviewed on YouTube if Perplexity matters to you. Perplexity linked a YouTube video in 88 of its 160 answers. Send products to creators who review your category, and ask them to label paid partnerships.
Join Reddit threads openly. Answer questions in the communities listed above from an account that says who you are. Do not seed fake threads: it is a brand risk, and nothing in this data shows such threads get cited.
Check that ChatGPT can reach your site. Your robots.txt file tells bots which pages they may fetch. OpenAI says sites that opt out of OAI-SearchBot will not be shown in ChatGPT search answers, apart from navigational links.
Measure your own category. This study covered 10 questions, and your category's list will differ. Citation gap analysis lists the websites that recommend your competitors but never mention you. The AI action plan ranks up to 12 places to get mentioned. For the longer playbook, read how to get cited by AI.

How we counted, and the limits

We read the stored response for all 480 answers and took every link each answer showed as a source. The website is the link's hostname without "www". Our totals match the study's published figures exactly: 4,640 citations across 247 websites. The engine and language splits, the type shares and the "best" or "top" URL count were computed from the same raw files on 15 September 2026. A URL counts as a list page when its path contains the word "best" or "top".

The type shares by engine and language use one set of labels. The 20 websites in the table keep the types shown there, in four groups: brand websites, publishers and blogs (news, lifestyle, review, company and Medium blogs), marketplaces, and social platforms. Every other website takes its label from the study's published domain list in the repository, with two changes: thedeconstruct.in counts as a brand website, and 7 smaller sites from its marketplace list, such as nalli.com and dropy.in, stay unlabelled because their cited pages did not show that they sell many brands. That published list also counts magicpin.in and smytten.com as marketplaces. We count them as blogs, as the table does, so our marketplace shares are lower than that list would give. The raw answers and analysis code are in the public IndicGEO repository, so anyone can recompute these numbers.

ChatGPT and Gemini answers were captured from their consumer web apps, logged out. Perplexity answers came from its Sonar model with live web search, which may link differently from the Perplexity app. All were asked from inside India at country level.

The limits:

Two categories. Skincare and fashion only, 10 questions. Other categories may cite different websites.
One day. Every answer was collected in a 57-minute window on 14 August 2026. Answers can change from week to week.
Three engines. Google AI Overviews were not part of this study.
Links shown, reasons unknown. A citation is a link the answer displayed. It does not prove that page caused a brand to be named.
Our interest. Depra ran and paid for this study and sells AI visibility tracking. The raw data is public.

To see which websites AI cites for your own buyers' questions, and which of them name your competitors and leave you out, start 7 days free on any plan, no card. Your first scan starts at signup.

Frequently asked questions

Which websites does ChatGPT cite?

In 160 ChatGPT answers to skincare and fashion buying questions asked from India on 14 August 2026, with web search on, ChatGPT showed 408 links across 76 websites, about 2.5 per answer. The most cited were brand websites and Indian news shopping pages, led by beminimalist.co (29 links), ndtvshopping.com (25), hindustantimes.com (25), pepejeans.in (17) and cetaphil.in (16). It linked YouTube twice and Reddit three times.

Does AI search use Reddit?

Yes, and how much depends on the engine. Reddit was linked in more answers than any other website in our study, 106 of 480. Almost all of that came from Perplexity, with 182 of Reddit's 191 citations. In the same questions ChatGPT cited Reddit 3 times and Gemini 6 times. The threads came from Indian communities such as r/IndianSkincareAddicts and r/IndianFashionAddicts.

Why does Perplexity cite more sources than ChatGPT?

Our data measures the gap and cannot show the cause. Across 80 English answers each, Perplexity showed 19.6 links per answer and ChatGPT showed 2.5. Perplexity's most linked websites were YouTube, Reddit, Amazon.in, Instagram and Flipkart, while ChatGPT linked mainly brand websites and news shopping pages. Our Perplexity answers came from its Sonar model with live web search, which may link differently from the Perplexity app.

How do I find which sites AI cites for my category?

Ask the questions your buyers ask, from inside your country, several times on each engine, and record every link each answer shows. One run is not enough because answers change between reruns. Depra does this daily for ChatGPT, Gemini and Google AI Overviews and weekly for Perplexity, labels every cited website by type, and lists the websites that recommend your competitors but never mention you.

Hinglish Changes Which Brands AI Recommends

ifham baig — Thu, 01 Oct 2026 08:38:04 +0000

Originally published on depra.ai on 15 Sept 2026.

Yes, AI engines recommend different brands when the same buying question is asked in Hinglish, and how much depends on the engine. In our study of 480 AI answers collected from India, Perplexity's brand list changed 23.4 points beyond normal rerun noise, Gemini's changed 7.8 points and ChatGPT's changed only 2.2 points.

Hinglish here means Hindi and English mixed and typed in English letters, like "konsa face wash kharidna chahiye". Rerun noise is how much an engine's brand list changes when you simply ask the identical question again. The second headline is about sources: Gemini showed at least one source link under 90.0% of English answers and under only 41.3% of Hinglish answers.

This post is the plain readout for brand and marketing teams. The Hinglish vs English AI shopping study page is the record of every table, interval and test, and the raw data is public in the IndicGEO repository on GitHub.

How we tested the same question in two languages

We wrote 10 buying questions: 5 about skincare and 5 about fashion. These are the prompts, meaning the exact text typed into each AI engine, written the way a buyer would type it. None of them named a brand. Each question had an English version and a Hinglish version with the same meaning, the same budget and the same India anchor. An independent reviewer checked every pair before analysis, and 3 fashion pairs were rewritten after failing that review.

One pair, word for word:

English: "Which face wash should I buy for oily skin in India under ₹500?"
Hinglish: "India mein oily skin ke liye 500 rupees ke andar konsa face wash kharidna chahiye?"

We asked each question 8 times in each language on three engines:

ChatGPT, captured from the chatgpt.com web app with web search switched on.
Gemini, captured from the gemini.google.com web app.
Perplexity, through Sonar with live web search switched on. Sonar is the answer model Perplexity offers to developers, reached through its API instead of the perplexity.ai app.

Every request was located in India and started fresh, with no chat history and no logged-in account. All 480 answers were collected on 14 August 2026 inside one 57-minute window, between 12:25 and 13:22 UTC. The order was shuffled, so neither language got a different time of day.

Brands were counted by matching each answer against a brand list built from all 480 answers and checked by hand. Some brand names are also everyday Hindi words. "Bata" is a shoe brand and also the Hinglish verb for "tell". Those names counted only when capitalised as a brand, so Hinglish answers were not over-counted.

Why rerun noise is the baseline

An AI engine rarely gives the same answer twice. English and Hinglish answers will always differ a little, even if language has no effect. So we measured two things per engine:

How much the brand list overlaps between reruns of the same question in the same language.
How much it overlaps between the English and the Hinglish version of that question.

The overlap score is the number of brands two answers share, divided by all brands either answer named. The language effect is the gap between the two overlaps. A gap of zero means language changes nothing beyond chance.

This follows a point made in Don't Measure Once, a 2026 paper by Schulte, Bleeker and Kaufmann: AI answers vary across runs, prompts and time, so one answer is an unreliable measure of a brand's visibility.

Which AI engine changes its brand recommendations most?

Perplexity, by a wide margin.

Engine	Brand overlap between reruns	Brand overlap, English vs Hinglish	Language effect	95% range
ChatGPT	54.7%	52.5%	2.2 points	0.3 to 4.6
Gemini	51.2%	43.3%	7.8 points	2.6 to 13.9
Perplexity	68.3%	44.9%	23.4 points	12.9 to 35.1

Basis: 480 answers, 80 per engine and language (10 prompts x 8 runs), India-located, 14 August 2026. The 95% range is the span the true language effect most likely falls in, given that the study used only 10 prompts. It comes from resampling the 10 prompts 10,000 times.

Perplexity is the most consistent engine on reruns, at 68.3% brand overlap, and also the most sensitive to language. When the most self-consistent engine drops to 44.9% overlap across languages, chance is a poor explanation. Its choice of which brand to name first also shifted: the first brand matched in 69.5% of rerun pairs and 49.8% of English and Hinglish pairs.

Gemini sits in the middle at 7.8 points. Its first-named brand matched in 63.2% of rerun pairs and 42.2% of cross-language pairs.

ChatGPT barely moves. The study fixed its test before looking at the data: a one-sided test, which only asks whether English and Hinglish answers overlap less than reruns do. The 2.2-point gap passed it (p = .046, meaning a gap this size would show up by chance less than 5 times in 100 if language made no difference). It is still the weakest result in the study. The whole gap came from the skincare prompts and 4 of the 10 prompts went the other way. A two-sided test, which also allows for Hinglish overlapping more, would not pass (p = .092). The fair reading is that Hinglish ChatGPT answers named close to the same brands as English ones for these questions.

The sources behind the answers follow the same order. The overlap in cited websites between languages fell 3.1 points on ChatGPT, 22.3 points on Gemini and 32.4 points on Perplexity, each measured against the same rerun baseline. If you track several engines, why AI engines disagree about your brand covers what drives those differences.

Which brands appeared or vanished in Hinglish answers?

The engine-level gaps are averages. For single brands, the swings on Perplexity were large. Each figure below is the share of one engine's 80 answers in one language that named the brand.

Perplexity

Brand	English answers	Hinglish answers	Change
The Derma Co	2.5% (2 of 80)	20.0% (16 of 80)	+17.5 points
Deconstruct	3.8% (3 of 80)	16.3% (13 of 80)	+12.5 points
Minimalist	22.5% (18 of 80)	33.8% (27 of 80)	+11.3 points
Plum	28.7% (23 of 80)	15.0% (12 of 80)	-13.7 points
La Roche-Posay	13.8% (11 of 80)	1.3% (1 of 80)	-12.5 points
Taneira	15.0% (12 of 80)	0.0% (0 of 80)	-15.0 points

Gemini

Brand	English answers	Hinglish answers	Change
Dot & Key	10.0% (8 of 80)	21.3% (17 of 80)	+11.3 points
Allen Solly	5.0% (4 of 80)	15.0% (12 of 80)	+10.0 points
Minimalist	45.0% (36 of 80)	36.3% (29 of 80)	-8.7 points
CeraVe	10.0% (8 of 80)	0.0% (0 of 80)	-10.0 points

On ChatGPT, no brand moved by more than 10 points. The largest change was Deconstruct, from 10.0% to 18.8% of answers.

One pattern stood out on Perplexity. Brands that lost ground in Hinglish leaned international or premium, such as La Roche-Posay and Taneira, a premium Tata brand. Brands that gained leaned toward mass-market Indian D2C brands, such as The Derma Co, Deconstruct and Minimalist. D2C, short for direct-to-consumer, describes brands built to sell online straight to shoppers. The share of named brands that are Indian rose in Hinglish on Gemini, from 75.6% to 82.0%, and on Perplexity, from 63.7% to 66.3%. On ChatGPT it stayed flat, 74.4% to 74.7%. We observed this tendency and did not test it as a hypothesis.

Treat these as observations about AI shopping recommendations in India, from 10 questions on one day. They are not endorsements and they say nothing about which product is better. The same brands could score very differently on another set of questions.

Gemini cites far fewer sources in Hinglish

A citation is a source link an engine shows with its answer. Gemini attached at least one to 72 of 80 English answers (90.0%) and to only 33 of 80 Hinglish answers (41.3%). Its average number of links per answer fell from 9.4 to 5.2. Its Hinglish answers were also 33% shorter, averaging 3,099 characters against 4,609 in English.

ChatGPT and Perplexity attached sources to every answer in both languages, 80 of 80 each. What changed on those two engines was which websites they cited.

The study did not test why Gemini behaves this way. What it recorded: 47 of 80 Gemini Hinglish answers showed no source links at all, against 8 of 80 English answers.

Do AI engines answer Hinglish questions in Hinglish?

Almost always.

Engine	Asked in English	Asked in Hinglish
ChatGPT	80 of 80 in English	80 of 80 in Hinglish
Gemini	80 of 80 in English	71 in Hinglish, 8 in Devanagari, 1 in English
Perplexity	80 of 80 in English	80 of 80 in Hinglish

Devanagari is the script Hindi is usually printed in. Gemini used it for 8 of its 80 Hinglish answers, a script the person asking did not type. The language labels were checked against human judgement on a sample of 30 answers and agreed on all 30.

So a Hinglish buyer reads a Hinglish answer. On Perplexity and Gemini that answer also links to a different set of websites and names a different set of brands than the English one.

What does this mean for a brand selling in India?

If you track English prompts only, you are measuring what English buyers see and leaving out Hinglish AI search. On Perplexity and Gemini, Hinglish buyers see a different brand list. Five steps follow from the data:

Write each buying question twice. Keep the budget, category and intent identical in English and Hinglish. Our guide to writing Hinglish tracking prompts covers the wording.
Read each engine on its own. The language effect ran from 2.2 points on ChatGPT to 23.4 points on Perplexity. An average across engines hides the one where you lose most.
Ask more than once before trusting a number. Rerun overlap was 51% to 68% for brands, so a single answer tells you little.
Compare your English and Hinglish share with your top competitors. A split like The Derma Co's 2.5% against 20.0% on Perplexity is the kind of gap to look for.
Check which websites get cited in each language. Cited-site overlap between languages fell 32.4 points on Perplexity and 22.3 points on Gemini, so the pages that earn you mentions in English may not be the ones Hinglish answers link to.

Depra runs this as a product. Hinglish AI visibility tracking is on every plan: Depra suggests Hinglish versions of your buying questions when India is one of your markets, detects the language of every answer, and your report shows an English vs Hinglish table. ChatGPT, Gemini and Google AI Overviews, the AI-written summary Google shows above some search results, are checked daily. The Perplexity visibility tracker runs weekly. Plans start at ₹1,999 a month plus 18% GST, listed on the pricing page. For the basics of reading these numbers, see how to track brand mentions in AI answers.

Limits of the study

10 prompts in 2 categories. The results describe brand-free buying questions about skincare and fashion in India. Other categories may behave differently.
One fieldwork window. Engines change week to week, so this is a snapshot from 14 August 2026. A rerun of 2 prompts 30 to 60 minutes later pointed the same way on all three engines, which covers drift within the hour only.
One style of Hinglish. Urban, typed in English letters, with 43% to 69% Hindi words per prompt. Devanagari Hindi and other Indian languages were out of scope.
Logged-out sessions. Signed-in users with a chat history may see different answers.
Displayed sources only. A citation records what the engine linked, and does not prove why a brand was named.
ChatGPT is the weakest finding. Its 2.2-point gap rests on the skincare prompts alone.
Funding interest. Depra ran and paid for the study and sells AI visibility tracking. To offset that, every raw answer, the analysis code, the brand list and the method, fixed before any analysis, are public in the IndicGEO repository, and one command recomputes every table in this post.

To see your own brand's English and Hinglish numbers across ChatGPT, Gemini, Perplexity and AI Overviews, start 7 days free on any plan, no card. Your first scan starts at signup.

Frequently asked questions

Does AI recommend different brands in Hinglish?

Yes, and the size of the change depends on the engine. In a study of 480 AI answers collected from India on 14 August 2026, the brand list changed beyond normal rerun noise by 23.4 points on Perplexity, 7.8 points on Gemini and 2.2 points on ChatGPT.

Does ChatGPT recommend different brands in Hinglish?

Barely. ChatGPT's brand list changed 2.2 points more between English and Hinglish than between two runs of the same question. That gap came only from the skincare prompts and is the weakest finding in the study, so for these buying questions ChatGPT gave close to the same brands in both languages.

Which AI engine is most affected by Hinglish prompts?

Perplexity. Its brand list changed 23.4 points beyond rerun noise, against 7.8 for Gemini and 2.2 for ChatGPT. The 95% range for Perplexity was 12.9 to 35.1 points, which is the span the true effect most likely falls in given only 10 prompts. On Perplexity, The Derma Co appeared in 2.5% of English answers and 20.0% of Hinglish answers.

Why does Gemini cite fewer sources for Hinglish questions?

The study measured the drop and did not test the cause. Gemini showed at least one source link in 72 of 80 English answers (90.0%) and 33 of 80 Hinglish answers (41.3%). Its Hinglish answers also carried fewer links on average, 5.2 against 9.4, and were 33% shorter.

Can I see the raw data?

Yes. All 480 raw answers, the prompt pairs, the brand list, the analysis code and the method, which was written and fixed before any analysis, are public in the IndicGEO repository on GitHub (github.com/ifham001/IndicGEO), with code under MIT and data under CC BY 4.0. Running the analysis script recomputes every table in this post from the raw answers.

How to Measure AI Visibility

ifham baig — Thu, 01 Oct 2026 08:30:41 +0000

Originally published on depra.ai on 14 Aug 2026, updated 15 Sept 2026.

The metrics, sample sizes and ranges that turn AI answers into a number you can defend, with worked examples from our English and Hinglish study.

What is AI visibility?

AI visibility, also called AI search visibility, is how often AI assistants mention your brand when people ask them questions in your category. Each question you send to an AI engine is called a prompt. The main engines are ChatGPT, Gemini, Perplexity and Google AI Overviews. An AI Overview is the AI-written summary Google shows above some search results.

It differs from a Google ranking in three ways:

An AI answer has no fixed position 1, and its length and order change between runs.
The same question can name different brands minutes apart.
Each engine picks its sources in its own way, so a strong result on one engine predicts little about the next.

So measure AI visibility as a rate across many answers.

Why can't one AI answer measure your visibility?

Three public studies show how much answers move:

SparkToro and Gumshoe had 600 volunteers run 12 prompts through ChatGPT, Claude and Google's AI tools 2,961 times in November and December 2025. The same list of brands came back less than 1% of the time. The same list in the same order came back about once in 1,000 runs.
SISTRIX tracked 82,619 prompts for 17 weeks, from December 2025 to April 2026. In Germany, 74% of the sources ChatGPT cited were new from one week to the next. In the UK the figure was 60%.
Kevin Indig's analysis of Omnia data covered 3.7 million citations from 20,000 prompts. Only 2.37% of cited URLs appeared in all three of ChatGPT, Perplexity and Google AI Overviews for the same prompt. 91% appeared in just one engine.

So one screenshot proves nothing in either direction. SparkToro also found that how often a brand appears across many runs is more consistent than the order it appears in.

How is AI visibility measured?

Write each prompt the way a buyer would type it. Pick 20 to 50 buying prompts, ask each one on every engine you care about, and repeat on a schedule. Leave out questions that contain your brand name. A question like "is Brand X any good?" almost always returns Brand X and inflates the score.

From the stored answers, calculate four AI visibility metrics for each engine:

Metric	What it answers	How to calculate it	How to read it
Visibility	How often does AI name us?	Answers that name you, divided by all answers	Always with the answer count and a range
Share of voice	Of all brand mentions, how many are ours?	Your mentions, divided by mentions of every tracked brand	Same competitors, same questions
Average position	When we are named, how early?	Mean order of first mention, where 1 is named first	Only next to visibility
Sentiment	How does AI describe us?	Positive, neutral or negative, with the quoted sentence	Watch the direction over weeks

A worked example: 40 prompts asked 3 times each on ChatGPT give 120 answers. If 74 of them name you, your ChatGPT visibility is 62%.

Share of voice is your share of all brand mentions in your category's answers. Because it compares you with competitors in the same answers, a model update that moves every brand at once changes it less. AI share of voice explained covers the calculation in detail.

Read position only next to visibility. Being named first in 8% of answers is weaker than being named third in 55% of them.

Report every engine separately. A brand at 70% on Gemini and 10% on Perplexity averages 40%, the same as a brand at 40% everywhere, and the two need very different work. AEO vs GEO vs SEO explains why engines diverge.

Add one more input: cited sources, the websites an answer links to. Ahrefs studied 75,000 brands and found that branded web mentions correlated with brand mentions in Google AI Overviews at 0.664, against 0.218 for backlinks. The link is a correlation and does not prove cause. It still makes the sites an engine keeps citing a practical list of places to get mentioned.

How many answers do you need?

Every visibility number needs two companions. The first is the number of answers behind it, which statisticians write as n. The second is a 95% confidence range: the band the true rate most likely sits in. We calculate ranges with the Wilson score interval, a standard formula for a percentage that stays accurate near 0% and 100%.

Go back to the example at the top: 28 answers that name you out of 45. Visibility is 62%, and the 95% range runs from about 48% to 75%. If next month reads 68%, that sits inside the same band, so nothing has changed that you can detect. At the same 62% rate, 140 answers narrow the range to about 54% to 70%.

Here is how the range shrinks at 50% visibility:

Answers behind the number	95% range
20	30% to 70%
80	39% to 61%
400	45% to 55%
1,000	47% to 53%

Two rules follow. Publish the range with the number: "62% of 45 answers, range 48% to 75%" can be checked, and "62%" cannot. Count a move as real only when the new range does not overlap the old one. For how much a single question's answers vary, read whether ChatGPT gives the same answer to everyone.

What do 480 AI answers from India show?

The English vs Hinglish AI shopping study asked 10 buying prompts, 5 in skincare and 5 in fashion, in English and in Hinglish, 8 times each, to ChatGPT, Gemini and Perplexity from inside India. Fieldwork ran on 14 August 2026 and produced 480 answers: 80 per engine in each language. Hinglish is Hindi and English typed in English letters, the way many Indian buyers write ("sabse accha protein powder kaunsa hai").

Some gaps are real and some are noise. We put 95% ranges on four brand rates from the study:

Engine and brand	English	Hinglish	Ranges overlap?
Perplexity, The Derma Co	2.5% (2 of 80), range 1% to 9%	20.0% (16 of 80), range 13% to 30%	No, a clear difference
Perplexity, Taneira	15.0% (12 of 80), range 9% to 24%	0.0% (0 of 80), range 0% to 5%	No, a clear difference
Gemini, Minimalist	45.0% (36 of 80), range 35% to 56%	36.3% (29 of 80), range 27% to 47%	Yes, too close to call
ChatGPT, Minimalist	36.3% (29 of 80), range 27% to 47%	42.5% (34 of 80), range 32% to 53%	Yes, too close to call

An 8.7-point drop for Minimalist on Gemini sounds like news. At 80 answers per language, it sits inside normal variation. The Derma Co's 17.5-point rise on Perplexity clears it.

Citations change with language. Gemini attached at least one source to 90.0% of English answers (72 of 80) and to 41.3% of Hinglish answers (33 of 80).

Method and limits. Brand rates and citation counts come from the study's published results. We calculated the Wilson ranges on 15 September 2026 from the published counts. Each brand rate is out of all 80 answers for that engine and language, including prompts from the other category. The study covers 10 prompts in 2 categories, one fieldwork day and one urban Hinglish register. ChatGPT and Gemini answers were captured from their consumer web apps without a signed-in account, and Perplexity answers came from the answer model Perplexity sells through its API, with live web search, so signed-in users may see different answers. Depra funded and ran the study and sells AI visibility tracking. The raw answers, code and prompts are public on GitHub so anyone can recheck them.

Why measure English and Hinglish separately?

Prompt language changes which brands some engines recommend. In the same study, the brand list changed between English and Hinglish by more than normal rerun noise: 2.2 points on ChatGPT, 7.8 on Gemini and 23.4 on Perplexity.

If you blend both languages into one number, a brand that gains in Hinglish and loses in English can look flat. Track English and Hinglish as separate prompt sets with separate scores, and write each Hinglish prompt so it asks exactly what its English pair asks. Hinglish AI visibility tracking shows how the split appears in a report.

How to track AI visibility over time

Freeze the prompt set. Editing wording mid-quarter resets your baseline. Add new prompts as a separate group and compare them from their start date.
Repeat on a schedule. Daily where you can, weekly at minimum.
Use a rolling window. Compare the last 7 or 28 days with the period before, so single-day swings average out.
Compare ranges. Count a change only when the two periods' ranges do not overlap.
Note model release dates. A step change on the day an engine ships a new model may be the engine changing, with nothing to do with your content.
Track competitors on the same questions. Your share of voice against the same brands tells you more than your rate alone.

A spreadsheet handles one engine and about 20 questions. Tracking brand mentions in AI answers walks through that setup.

Depra asks each prompt to ChatGPT with web search, Gemini and Google AI Overviews every day, and to Perplexity every week, from inside the country you pick. It stores every full answer and compares each score with the previous period. The daily brief, a page inside the app, flags a change only when it is bigger than normal run-to-run noise. See AI visibility tracking in Depra and the full rules in Depra's scoring rules.

How can I check my AI visibility for free?

You can run a rough check by hand:

Write 10 buying questions a customer would ask, with no brand names in them.
Open ChatGPT, Gemini and Perplexity in a private browser window, signed out where the engine allows it.
Ask each question 3 times on each engine, starting a new chat every time.
Search Google for the same questions and note whether an AI Overview appears and whether it names you.
Log every answer in a spreadsheet: date, engine, question, run, brands named in order, the sentence about you, and the links cited.
For each engine, divide the answers that name you by all answers.

That gives 30 answers per chat engine. At 50% visibility, 30 answers carry a range of about 33% to 67%, which is fine as a baseline and too wide to spot a month-to-month change. Your location and browser can also change what you see.

Depra has no free plan. Every plan starts with 7 days free and no card, and your first scan starts at signup. Starter costs ₹1,999 a month plus GST and tracks 20 prompts, about 1,900 AI answers a month in one market. Read how Depra's AI visibility check works or compare plans on the pricing page.

What should an AI visibility report include?

A useful AI visibility report answers "are we showing up, against whom, and what do we fix next" with numbers a reader can check:

A one-line summary: visibility, the number of answers behind it, and your rank among tracked brands.
Visibility for each engine, with its confidence range.
Share of voice and average position against the same competitors.
Sentiment, with the sentences AI used about you.
The websites answers cite, including sites that name your competitors and never you.
English and Hinglish side by side, if you sell in India.
Change against the previous period, marked only when it clears normal noise.
The 3 to 5 fixes to work on next.

When you compare tools, check that each one shows answer counts and ranges, reports engines separately, runs competitors on identical prompts and leaves out questions that name your brand.

Depra's report is one printable page per project with a plain-English summary, visibility by engine with ranges, an English vs Hinglish table, competitors and your top fixes. Agencies can add their own logo, accent colour and footer, then save it as a PDF from the browser. Depra for agencies covers client reporting.

Start 7 days free on any plan, no card. Add your buying questions and competitors, and your first scan starts at signup.

Frequently asked questions

What is AI visibility?

AI visibility is the share of AI answers to your buyers' questions that name your brand. It is measured per engine, such as ChatGPT, Gemini, Perplexity and Google AI Overviews, across many repeated answers, because the same question can return different brands each time it is asked.

How is AI visibility measured?

Ask a fixed set of buying questions that do not contain your brand name to each AI engine, repeat them on a schedule, and store every answer. Visibility is the number of answers that name you divided by all answers. Report it per engine with the number of answers behind it and a 95% confidence range, next to share of voice, average position, sentiment and the websites the answers cite.

How to track AI visibility?

Freeze your question set, ask it daily or weekly on each engine, and compare a rolling 7 or 28 day window with the previous one. Track your competitors on the same questions. Count a move as real only when the new confidence range does not overlap the old one. A spreadsheet works for one engine and about 20 questions. Past that, a tool that stores every answer saves the manual work.

How can I check my AI visibility for free?

Write 10 buying questions without your brand name, ask each one 3 times in a new chat on ChatGPT, Gemini and Perplexity, and check Google for an AI Overview. Log which brands each answer names and divide your mentions by total answers per engine. That gives a rough baseline. Depra has no free plan. Every plan starts with 7 days free and no card.

How many AI answers do you need to measure visibility?

More than most people expect. At 50% visibility, 20 answers give a 95% range of about 30% to 70%, 80 answers about 39% to 61%, and 400 answers about 45% to 55%. For month-to-month tracking on one engine, a few hundred answers is a sensible floor.

How to Get Your Brand Cited in AI Answers

ifham baig — Thu, 01 Oct 2026 08:28:52 +0000

Originally published on depra.ai on 14 Aug 2026, updated 15 Sept 2026.

What decides which websites AI engines cite, what the public research says works, and where 4,640 citations in Indian shopping answers pointed.

To get cited by AI, get your brand named on the pages ChatGPT, Gemini, Perplexity and Google AI Overviews already use as sources for your category: best-of lists, marketplace listings, review videos and forum threads. Then make your own pages easy to quote, and check each engine's sources every week, because they change fast.

A citation is a link an AI engine shows as a source for its answer. Google AI Overviews are the AI-written summaries that appear above the normal results on some Google searches. A prompt is one question typed into an AI engine, and tracking tools count the questions they monitor as prompts. This guide uses public research we opened and checked on 15 September 2026, plus 4,640 citations from our own study of 480 AI shopping answers asked from India.

What makes AI cite a website?

An AI engine with web search runs searches behind your question, reads a small set of results and writes its answer from the text inside them. The pages it links are the pages it drew from. So a page gets cited when two things are true: it shows up in those searches, and it contains a clear answer to the question.

Take a buying question such as "best face wash for oily skin under ₹500". The pages that answer it are lists of brands, product listings with prices, review videos and threads where buyers compare options. A brand homepage rarely answers it. That is why most of the work to get cited happens on other people's websites.

If AI never names your brand at all, start with why ChatGPT doesn't mention your brand before you spend on outreach.

Do backlinks or brand mentions matter more for AI citations?

Brand mentions. Ahrefs studied 75,000 brands and found that brand web mentions correlated with visibility in Google AI Overviews at 0.664, against 0.218 for backlinks (published May 2025). Ahrefs used a Spearman correlation: a score where 1 means brands that rank higher on one measure always rank higher on the other, and 0 means no relationship. Mentions sat much closer to 1 than links did. A mention counts without a link.

Two limits apply. The study covered Google AI Overviews only. And a correlation does not prove cause, since brands with many mentions also tend to be bigger brands. Even so, the result points toward PR, reviews and community presence over link building.

A mention is any page that names your brand in text: a review, a news story, a best-of list, a marketplace listing or a Reddit comment. Keep a list of them the way you would keep a list of links: which websites, how recent, and whether each one describes your product, price and category correctly.

Does each AI engine cite different sources?

Yes, by a wide margin. Kevin Indig took a random sample of 20,000 prompts from Omnia, a platform that tracks AI search answers, and found that only 2.37% of cited URLs appeared in all three of ChatGPT, Perplexity and Google AI Overviews for the same prompt. 91.07% appeared in exactly one engine. The sample ran mostly in European markets.

Our India data shows the same split. The three engines in our study cited 600 distinct pages. 6 of them (1.0%) were cited by all three engines, and 539 (89.8%) by only one. At website level, 20 of 247 websites (8.1%) were cited by all three.

The engines also link sources at very different rates:

Engine	Answers	Citations	Per answer	Most cited website
ChatGPT with web search	160	408	2.55	beminimalist.co (29)
Gemini	160	1,164	7.28	indisangam.com (99)
Perplexity	160	3,068	19.18	youtube.com (518)

Gemini attached no source at all to 55 of its 160 answers. Most of those were Hinglish answers: Gemini cited sources in 90.0% of English answers and 41.3% of Hinglish answers. A plan built on one engine's sources will miss most of the others, so decide which engines your buyers use and work through each one's list.

Where does AI get its sources in India?

In our study, YouTube, Reddit and Nykaa were the three most cited websites. We asked 10 buying questions (5 skincare, 5 fashion) in English and Hinglish, 8 times each, to ChatGPT, Gemini and Perplexity from inside India on 14 August 2026. Hinglish is Hindi and English typed in English letters, the way many Indian buyers write. The 480 answers carried 4,640 citations:

youtube.com: 534 citations, 518 of them from Perplexity. ChatGPT linked YouTube twice.
reddit.com: 191 citations, 82 of them to r/IndianSkincareAddicts. Reddit appeared in 106 of the 480 answers, more than any other website.
nykaa.com: 176 citations, and one of the few websites all three engines cited often: 14 times on ChatGPT, 83 on Gemini and 79 on Perplexity.

ChatGPT leans on different websites. India is ChatGPT's second-largest user base after the United States, with 100 million weekly users as of February 2026. Its most cited websites in our study were beminimalist.co, hindustantimes.com and ndtvshopping.com: a brand's own site and two news publishers. YouTube and Reddit barely register on ChatGPT: 2 and 3 of its 408 citations.

Three more findings decide where to start.

Your own website can be cited on every engine. Of the 6 pages all three engines cited, 3 sit on a brand's own website: a Cetaphil article on top body moisturisers, a Deconstruct article on sunscreens without a white cast, and Levi's men's jeans page.

List pages carry the most weight. About 4 in 10 citations went to addresses with "best" or "top" in them, and Gemini leaned on them most. The full top 20 websites, split by engine and by language, with the page types behind each count, are in which websites AI cites for shopping in India.

Hinglish questions pull different sources on Perplexity and Gemini. Overlap here means the websites both answers cited, as a share of every website either answer cited. On Perplexity, two runs of the same question in the same language overlapped by 85.2%, and an English run against a Hinglish run by 52.8%. On Gemini the figures were 33.1% and 10.8%. ChatGPT changed little. If Perplexity or Gemini matter to your buyers, keep your Hinglish questions as a separate list and compare its sources with your English list.

How we counted, and the limits. We read the stored source links for all 480 answers on 15 September 2026. We counted a website by its domain name, such as nykaa.com, and a page by its web address with everything after the "?" removed. YouTube links keep their video ID, so each video counts once. The cross-engine page overlap pools all 10 questions, so it is looser than a same-question comparison. The data covers 2 categories, 1 country, 1 day and 3 engines, with no Google AI Overviews and no signed-in account. Citations show what an engine linked, and they do not prove why a brand was named. Depra ran the study and sells AI visibility tracking. You can recheck every count from the raw answers in the IndicGEO repository on GitHub.

What on-page changes help AI cite your pages?

On-page work is cheap, so do it once. The GEO paper by Aggarwal et al. found that adding citations, quotations and statistics raised a page's visibility in AI answers by up to about 40% in a GPT-3.5 test setup (KDD 2024).

Later research is more cautious. C-SEO Bench (NeurIPS 2025) tested published conversational SEO methods, which rewrite a page to make it more persuasive to an AI model. Most were largely ineffective and often lowered the page's rank inside the answer. Traditional SEO, which gets the page into the results the model reads, worked significantly better.

Put the two findings together: get the page into search results first, then make it easy to quote. In practice:

Answer the buying question in the first 2 sentences under a question-shaped heading.
State your category, price band and who the product is for in plain text.
Put specs, prices and ingredients in text or tables, as well as in images.
Give numbers with dates and link your sources.
Publish an honest comparison or best-of page for your category that lists rivals next to your product. Our data shows engines cite these pages on brand websites.

Here is the difference on a product page. A line such as "our best-selling serum, loved by thousands" gives an engine nothing to quote. A line such as "vitamin C serum, 30 ml, ₹599, for oily skin, 10% vitamin C" answers a size, a price, a skin type and an ingredient question at once. The second line is the one that matches a buyer's question.

What is not worth your time?

llms.txt is the clearest case. An llms.txt file is a plain-text guide to your site written for AI tools. Ahrefs checked 137,210 domains and found that 97% of llms.txt files received zero requests in May 2026. AI retrieval bots made 1.1% of the requests that did arrive. Publishing one is harmless, but it does not count as progress. We looked at the evidence in more detail in does llms.txt work.

The second waste is trusting one screenshot. SparkToro had 600 volunteers run 12 prompts through ChatGPT, Claude and Google's AI tools 2,961 times in November and December 2025. The chance of two answers giving the same brand list was under 1 in 100. The same list in the same order came up about once in 1,000 runs. One answer that names you is a sample of one. We cover the reasons in does ChatGPT give the same answer to everyone.

How long does an AI citation last?

In ChatGPT, often less than a week. SISTRIX tracked 82,619 prompts over 17 weeks, from 17 December 2025 to 8 April 2026 in six countries, and found that ChatGPT Search replaced 74% of its cited domains each week. In Google AI Mode, the chat-style search mode inside Google, the figure was 56%.

A one-off PR push therefore gives a short spike. What holds up is a steady flow of new mentions on the websites each engine keeps returning to, checked every week.

How to get cited by AI: a 7-step plan

Freeze a question set. Write 20 to 40 buying questions your customers ask, none containing your brand name. If you sell in India, add Hinglish versions. Keep the set fixed so before-and-after comparisons hold.
Measure a baseline per engine. Record how often each engine names you and which pages it cites. Run every question more than once, because the same question gets a different answer each time.
List the websites each engine cites for your category. Rank them by how often they appear.
Find the gaps. Mark the websites and pages that cite your competitors and never mention you. That is your outreach list.
Get into the list pages. Send the authors of cited best-of articles samples, prices and facts they can check, and say which list you want to appear in. Start with lists that more than one engine cites.
Work the marketplaces, videos and threads. Grow recent reviews on Nykaa, Amazon and Flipkart, get honest YouTube reviews, and answer real Reddit threads with your affiliation disclosed.
Do the on-page work once, then keep measuring. Check each engine's sources every week and compare your visibility month to month. Act on moves larger than normal rerun noise.

For the recommendation side of the same work, read how to get ChatGPT to recommend your brand.

How to see which sites AI cites for your category

You can do it by hand:

Ask each engine your questions from a logged-out browser, so your own chat history does not shape the answers.
Run each question at least 3 times.
Copy every source link into a spreadsheet, with the engine, the question and the date.
Cut each link down to its domain name and count how often each domain appears.
Mark which domains name your competitors and which name you.

For 20 questions on 4 engines with 3 runs each, that is 240 answers to read, and SISTRIX's figures mean you would repeat it every week.

Depra's citation gap analysis does the counting. It lists the websites and pages AI cites when it recommends your competitors but never when it names you, at domain, subdomain and page level, with competitors' own websites filtered out. Each cited page carries a type label such as listicle, comparison, how-to or product page, so you can see which formats win in your category. If a competitor keeps getting named and you do not, the guide on why ChatGPT doesn't mention your brand has test prompts to find the cause.

How do you track AI citations?

Citation tracking means asking the same questions on a schedule and recording which websites each answer links. Depra asks your buyers' questions to ChatGPT with web search, Gemini and Google AI Overviews every day and to Perplexity every week, from inside your chosen country, in English and Hinglish. It stores every full answer with its sources, and you can export them as CSV.

On top of that record:

Each cited website carries a type label: editorial, company site, forum, reference, marketplace, directory, government or your own site.
A daily "what changed" page lists new and lost cited sources since the previous scan day.
The AI action plan ranks up to 12 places to get mentioned and up to 12 page formats to publish. After you mark an action done, it shows your visibility before and after. That view is a comparison over time, and it does not prove cause.
Depra suggests 5 topics with 8 buying questions each, none naming your brand, and it does not count questions that already contain your name.
Your report shows a confidence range, so you can tell a real move from rerun noise.

Starter costs ₹1,999 a month plus GST and includes 20 prompts, meaning 20 tracked questions. See how Depra stacks up against other trackers on the AI visibility tool comparison page.

Start 7 days free on any plan, no card. Your first scan starts at signup.

Frequently asked questions

How do I get cited by ChatGPT?

Get your brand named on the pages ChatGPT with web search pulls for your category questions. In our 160 ChatGPT answers asked from India on 14 Aug 2026, ChatGPT showed 2.55 source links per answer on average, and its most cited websites were beminimalist.co, hindustantimes.com and ndtvshopping.com. With that few links per answer, find the pages ChatGPT cites for your own buying questions and work on those first.

Which pages should I target to get cited instead of a competitor?

Start with the pages that already cite your competitor for your buying questions. In our India study, 39.5% of ChatGPT's citations pointed at URLs with best or top in the address, so best-of lists usually come first, then marketplace listings and brand blogs. Pitch the authors of those lists, or publish an honest comparison page on your own site that names your rivals.

Is llms.txt worth publishing to get cited by AI?

There is no public evidence that it helps. Ahrefs checked 137,210 domains and found that 97% of llms.txt files received zero requests in May 2026, and AI retrieval bots made 1.1% of the requests that did arrive. Publishing one is harmless. Spend the real effort on mentions in the pages AI engines already cite.

How long do AI citations last?

Often less than a week. SISTRIX tracked 82,619 prompts for 17 weeks and found that ChatGPT Search replaced 74% of its cited domains each week. Google AI Mode, the chat-style search mode inside Google, replaced 56%. Treat citations as ongoing work: keep earning new mentions and check your sources every week.

What is AI citation tracking?

AI citation tracking means asking AI engines the same buying questions on a schedule and recording which websites each answer links as a source. Depra does this every day for ChatGPT with web search, Gemini and Google AI Overviews and every week for Perplexity, stores every answer, and shows which websites cite your competitors but never mention you.

How AI search decides which brands to recommend (what we found in 480 ChatGPT, Gemini and Perplexity answers)

ifham baig — Thu, 01 Oct 2026 08:19:01 +0000

Disclosure: I'm Ifham, co-founder and CTO of Depra AI, an AI visibility tracking tool. The data below is from our own open study, and I link to the raw data so you can check it yourself.

A buyer opens ChatGPT and types "best face wash for oily skin under ₹500". The answer names two or three brands. If yours is not one of them, you never see that customer, and nothing in Google Search Console tells you it happened.

That is the problem answer engine optimization (AEO) and generative engine optimization (GEO) try to solve. This post covers what we measured across 480 AI answers, what it means for anyone shipping a website, and a short checklist you can run this week.

What are AEO and GEO?

Answer engine optimization (AEO) is the practice of structuring your content so AI assistants and answer boxes can extract a clear answer from it. Generative engine optimization (GEO) is the practice of making your brand more likely to be mentioned and cited inside AI-generated answers from ChatGPT, Gemini, Perplexity and Google AI Overviews. Classic SEO still matters, because these engines retrieve web pages, but ranking on page one is no longer the same thing as being recommended.

If you want the longer version, we wrote up the differences in AEO vs GEO vs SEO and what is AEO.

What we tested

We wanted to know whether the language of the question changes which brands an AI engine recommends. In India a lot of people type in Hinglish, which is Hindi written in Roman script and mixed with English, so this was not an academic question for us.

10 unbranded buying questions (5 skincare, 5 fashion), each written in English and in Hinglish
3 engines: ChatGPT, Gemini and Perplexity, geolocated to India
Each prompt run 8 times, 480 responses in total
All collected in one 57-minute window on 14 August 2026

The full write-up is on the Hinglish vs English AI shopping study page, and the data and code are on GitHub at ifham001/IndicGEO.

What we found

Gemini cited far fewer sources in Hinglish. 90.0% of English answers carried citations, against 41.3% of Hinglish answers. The average number of citations per answer dropped from 9.4 to 5.2.

Brand recommendations moved by engine. The gap between English and Hinglish recommendations was 2.2 percentage points on ChatGPT, 7.8 on Gemini and 23.4 on Perplexity (p = .0010). So the engine you are tracking changes the story completely, and an average across engines hides it.

Individual brands swung a lot. One brand went from 2 mentions to 16 on Perplexity depending on the language of the question, a swing of 17.5 percentage points.

A caveat before anyone quotes this: it is 10 prompts in two categories, in one country, in one time window. Treat it as directional evidence, not a law of nature. That is why we publish the data.

A checklist you can run this week

1. Let AI crawlers read your site

Many sites block AI bots by accident, through a blanket rule or a CDN setting. Check your robots.txt and decide deliberately. If you want to be retrievable by the major assistants, this is the starting point:

User-agent: GPTBot
Allow: /

User-agent: OAI-SearchBot
Allow: /

User-agent: PerplexityBot
Allow: /

User-agent: Google-Extended
Allow: /

There is a full list of AI crawlers and an AI crawler checker if you want to test what your own site returns.

2. Answer the question in the first two sentences

Engines pull passages. Put the direct answer at the top of the page, then the detail. Include the category, price and key specs as plain text, not only inside images or JavaScript widgets.

3. Add structured data that matches the visible page

JSON-LD gives machines an unambiguous summary of who you are. Keep it consistent with what a human can see on the page.

<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "Organization",
  "name": "YourBrand",
  "url": "https://www.example.com",
  "description": "One plain sentence saying what you sell and who it is for.",
  "sameAs": [
    "https://www.linkedin.com/company/yourbrand",
    "https://github.com/yourbrand"
  ]
}
</script>

4. Get mentioned where the engines already look

Your own site is only part of the picture. In our India data the most cited sources were YouTube, Reddit and Nykaa, which means third-party pages carry a lot of weight. Our guide on how to get cited by AI pulls together the research here, including an Ahrefs analysis of 75,000 brands that found brand mentions correlated with AI Overviews visibility much more strongly than backlinks did.

5. Measure per engine, per language, over time

AI answers are not stable. Sources that get cited one week are often replaced the next, and the same prompt can return a different brand list on every run. A single manual check tells you almost nothing. Run the same questions repeatedly, track each engine separately, and watch the trend rather than any one answer.

How we track this at Depra AI

Doing the measurement above by hand is slow, so we built Depra AI to automate it:

Tracks how ChatGPT, Gemini, Google AI Overviews and Perplexity mention your brand, in English and Hinglish
Shows which competitors appear for the same questions and which sources the engines cite
Ranks an action plan of pages to publish and places to get mentioned
Audits whether AI crawlers can reach your site
Excludes questions that name your brand from the score, so the number reflects real discovery

Plans start at ₹1,999 a month with a 7-day trial, and there is an AI visibility checker if you just want a first look. See pricing for details.

FAQ

Is AEO the same as SEO?
No. SEO aims to rank pages in a list of links. AEO and GEO aim to get your brand named and cited inside an AI-written answer. They overlap, because engines retrieve web pages, but the measurement is different.

Which AI engines should I track?
At minimum ChatGPT, Gemini, Perplexity and Google AI Overviews. In our study their behaviour differed a lot, so track each one separately.

Does the language of the question matter?
In our tests it did, especially on Perplexity and for Gemini's citation rate. If your customers mix languages, test the way they actually type.

How often should I check?
More than once. Answers vary between runs and between weeks, so a repeated schedule is more useful than a single snapshot.

If you run your own tests, I would like to see the results. Drop them in the comments, and tell me where you think our method is weak.