To be able to get cited in AI answers like ChatGPT or Gemini, Vietnamese brands need to follow Three gates to getting cited in AI. This article answers the next and more specific question: what changes when the query is asked in Vietnamese, and a guide on how to get cited in AI.
The short answer: query language is one of the strongest levers determining which brands get cited in AI, and it tilts toward local brands. But that advantage only materializes on some engines, and is neutralized on another for a technical reason few people know about.
Key Takeaways
- Moving from an English query to Vietnamese raises recommendation share by 0.80 for local brands, against only 0.15 for multinationals. Customers asking in Vietnamese is a structural advantage for you.
- 45% of brands show a home-language versus English sentiment gap above 0.15. Businesses measuring AI visibility only in English are underestimating themselves in their own home market.
- On ChatGPT, the language advantage can be neutralized at retrieval: 43% of background queries run in English even when the original prompt was in another language.
- Thin-web markets show a higher share of AI citations coming from the brand’s own website (around 15% against 12.5%), because AI has fewer third-party sources to read. Vietnamese sits in this group.
- The leading source is market-specific, not universal. Wikipedia leads in 11 of 12 languages, but in Poland recruitment portals outcite local Wikipedia by roughly 2.1 times.
Query Language Determines Which Brands Get Cited in AI
A favorable study on how language determines how brands get cited in AI answers
A study published on arXiv in June 2026 queried three grounded LLMs (GPT-5.4, Gemini 3.1 Pro and Perplexity Sonar Pro) about 66 brands across 11 markets, in 12 languages spanning four language families, producing 35,640 responses.
The core finding: query language shifts which brands are recommended and cited far more than it shifts how they are described. Specifically, moving from an English query to the brand’s home language raises recommendation share by 0.80 for local brands, but only 0.15 for global multinationals.
Translated into business terms: when your customers ask in Vietnamese, the structure of the system is tilting toward you rather than toward international brands. This is a structural advantage local brands already hold and typically do not exploit.
One consequence for measurement
The same study found that 45% of the 65 brands showed a home-language versus English sentiment gap above 0.15, with a mean absolute gap of 0.171. Which means businesses monitoring AI visibility only in English are looking at a distorted picture, and the distortion falls precisely on the visibility of locally headquartered brands.
If you are measuring AI visibility with an English prompt set, you are probably underestimating yourself.
Why the Language Advantage Disappears on ChatGPT
The language advantage above is real, but it only materializes if your Vietnamese sources make it into the candidate pool. On ChatGPT there is a technical step that determines AI citations.
Peec AI’s study of 10 million prompts and 20 million fan-out queries found that 43% of ChatGPT’s background queries run in English even when the original prompt was in another language, and 78% of non-English sessions included at least one English background query.
A concrete example from that research makes the consequence clear: Polish users asking about local auction platforms. ChatGPT’s English fan-out returned eBay instead of Allegro, the dominant platform in Poland. Allegro did not lose on content quality. Allegro lost because it never entered the candidate pool.
Two stages, two opposing mechanisms to get cited in AI
This explains a contradiction many people find confusing: why some research says AI favours same-language content while other research says AI is biased toward English.
Both are true, at different stages. At the retrieval stage, ChatGPT may translate the query into English, keeping Vietnamese sources out of the pool. At the citation stage, once sources are in the pool, the system favours sources matching the query language. Profound’s analysis of 3.25 billion citations confirms this: in research on the Mexican market, 96% of AI Overview citations came from Spanish-language sources.
If a local source is eliminated at stage one, the language-preference logic at stage two never gets a chance to operate.
Effort allocation by engine
| Effort | Google AI Overview | ChatGPT |
|---|---|---|
| Language mechanism | Favours sources in the query language | Translates the query to English at retrieval |
| Quality Vietnamese content | Primary lever | Necessary but not sufficient |
| Presence on English sources | Less critical | Condition for entering the pool |
If your customers search mainly through Google, well-structured Vietnamese content is sufficient and is an advantage. If they use ChatGPT heavily, you also need presence on the English-language sources a fan-out query might reach: LinkedIn, Clutch, G2, international trade publications. Even if your customers do not read English.
What Is Different in a Thin-Web Market Like Vietnam, and How Can Brands Get Cited in AI?
This is the part no competitor in Vietnam is discussing, and it changes how a widely quoted rule applies in this market.
The rule is that AI reads third-party sources far more than a brand’s own website. Research across 167,551 URL-grounded citations covering 128 brands and 12 markets puts the split at 85.7% against 14.3%. McKinsey reports an equivalent ratio, and Bain describes the same phenomenon differently: LLMs reach for reviews, earned media and comparison sites to describe a brand.
But that figure is an average across the whole sample, and that sample contains no market resembling Vietnam.
The research analyzed source split by language family and found a clear pattern: languages with a thinner native web show a higher share of AI citations coming from the brand’s own website than languages with a dense web.
Specifically, the Uralic group (Estonian, Finnish) sits at 15.5% and the Baltic group (Lithuanian, Latvian) at 15.0%, against the Slavic group (Polish, Czech, Slovak) at only 12.5%. The Uralic and Baltic groups also lean more on Wikipedia (5.0% and 4.3% against 4.0%).
The paper’s explanation: where the native web is thinner, AI has fewer third-party sources to read and falls back on the brand’s own site and the encyclopedia.
What this suggests for Vietnamese brands who want to get cited in AI
Vietnamese falls into the thin specialist-web group, particularly in B2B and technical sectors. If the pattern holds for Vietnamese, the consequences are:
Your website carries more weight in Vietnam than in Western markets. A Vietnamese business can influence AI’s answer about itself more than a comparable business in the US, simply because there are fewer competing sources for AI to read.
Wikipedia and reference sources carry more weight. In markets short on specialist sources, universal reference sources fill the gap.
The opportunity window has a deadline. As the Vietnamese-language web in your sector thickens, this ratio will move toward the mature-market level and the self-influence advantage will shrink.
A note on how certain this inference is
This is inferred from data on languages with comparable characteristics, not measured directly on Vietnamese. The underlying dataset covers 12 European markets and does not include Vietnam. Treat it as a well-grounded hypothesis to verify with your own industry data, not as an established conclusion.
The verification method is at the end of this article.
Four Source Types, and the Map to Increase AI Citations in Vietnam
The leading source is market-specific, not universal. This is what the data shows most clearly and also what is most commonly assumed wrong.
Across the 12-language dataset, Wikipedia was the most-cited domain in 11 languages, at shares from 3.71% to 5.70%. But the exception is the instructive part: in Lithuanian, the national business daily vz.lt beat Wikipedia at 4.38%.
Poland shows a sharper pattern still. The most-cited domain across 46 national brands was YouTube. Deeper in, four recruitment portals together supplied 637 citations against 297 for Polish Wikipedia, roughly 2.1 times as many.
Which means in a market with a strong employer-review ecosystem, AI reads recruitment sites to describe a brand. Where you stand on a careers portal is part of what AI says about you.
Four source types to audit to answer the question how to get cited in AI
1. Universal reference sources. Wikipedia leads in nearly every language. A missing or weak Wikipedia presence is a weak point in every language simultaneously. For brands that do not meet Wikipedia’s notability bar, other reference sources in the sector play a similar role.
2. Industry publications and business press. The vz.lt case in Lithuania shows that in smaller-language markets, the national business press fills space the encyclopedia does not cover. In Vietnam this group includes VnEconomy, CafeF, Doanh Nhan Sai Gon and specialist trade publications.
3. Market-specific platforms and directories. The Poland case shows this group can outrank Wikipedia. In Vietnam this might be comparison sites, B2B directories, or review platforms depending on sector. The key point: you have to measure to know, you cannot assume.
4. Brand-published content. Roughly 14% on average, but likely higher in a thin-web market like Vietnam. Not the largest share, but the one you fully control and can move fastest.
The most common mistake that reduces your AI citations
Assuming the leading source in your Vietnamese sector matches the leading source in the US market, or matches what you consider authoritative. The Poland data shows the leading source can be something entirely outside a conventional PR plan’s assumptions.
4 Steps to Increase AI Citations
This order is the reverse of what most businesses currently do.
Step 1: Measure AI citation possibilities before you write
Before producing any more content, establish which sources AI is reading to answer questions about your industry.
How: write 15 to 25 industry prompts in Vietnamese, exactly as your customers would ask. Run each prompt 3 to 5 times on ChatGPT and Google (multiple runs are mandatory, since AI output is probabilistic). For each run, record two things: which brands were named, and which sources were cited.
After one round you have your sector’s source map. That data drives every subsequent step.
Step 2: Make sure you are machine-readable to get cited in AI
If your site is not server-side rendered, has no schema, or still carries noindex tags, none of the content work behind it reaches its destination. This is a prerequisite, done once.
Details in Schema markup for AEO and the foundation conditions section of How to choose and hire an AEO service.
Step 3: Vietnamese content that fully answers each sub-question
Because query language tilts toward you, quality Vietnamese content is the highest-return lever among the things you control directly. Each section should fully answer one sub-question within its first 40 to 60 words.
Details in The 90-day content strategy and Google AI Overview.
Step 4: Get present on head-of-distribution sources
Using the source map from step 1, pick three to five sources from the head of the distribution and concentrate there. Do not spread evenly.
This is the slowest part (3 to 6 months) and also the part covering 86% of what AI reads.
Step 5: If targeting ChatGPT citations, add English-language signals
Per the fan-out mechanism covered above, Vietnamese content alone is not enough to enter ChatGPT’s candidate pool. Pick two or three authoritative English-language sources in your sector and build presence there, even if your customers do not read English.
For Google AI Overview, skip this step, since Vietnamese content is already the right lever.
FAQ
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How do Vietnamese brands get cited by ChatGPT and Gemini for industry queries?
Three things in order: make content machine-readable, publish Vietnamese content that fully answers each sub-question, and build presence on third-party sources. The third matters most, because 85.7% of citations about a brand point to third-party sources and only 14.3% to the brand’s own site.
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How does query language affect which brands AI recommends?
Substantially, and in favour of local brands. Research across 35,640 responses found that switching from an English query to the home language raises recommendation share by 0.80 for local brands, against 0.15 for multinationals.
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Why are Vietnamese brands often missing from ChatGPT answers?
The cause differs by engine. On ChatGPT, 43% of background queries run in English even when the original prompt was not, so local sources can be eliminated at the retrieval stage. On Google AI Overview, the issue is usually missing sub-question structure rather than language.
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What sources does AI read to answer about a brand?
85.7% third-party, 14.3% the brand’s own site. Sources are highly concentrated: 80% of citations come from about 18% of domains. Wikipedia leads in 11 of 12 languages studied, but the leading source varies by market. In Poland, recruitment portals outcite Polish Wikipedia by roughly 2 to 1.
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Is measuring AI visibility only in English sufficient?
No, and it can be seriously misleading. Research found 45% of brands show a home-language versus English sentiment gap above 0.15. Businesses measuring only in English underestimate their visibility in their own home market.
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Where should I start if my brand has never appeared in an AI answer?
Start by measuring which sources AI reads about your industry, not by writing more content. Run 15 to 25 Vietnamese industry prompts, 3 to 5 runs each, and record the cited sources. That list usually differs from the initial assumption.
