Why English-First Content Gives Singapore Businesses an Edge in AI Search

A pattern is emerging in how AI search systems retrieve content, and it is worth paying attention to if you run a Singapore business.

Peec AI, a company that tracks how AI search systems source and cite content, has been studying the background retrievals ChatGPT makes when answering questions, a process called "fan-out" queries. Their research into millions of ChatGPT prompts found that AI search systems disproportionately retrieve English-language content during this process, even when the original query was typed in another language.

The implication: a meaningful share of AI-driven content retrievals, regardless of what language the user typed in, pull from English-language sources.

For businesses in markets where you have to choose between writing English content and writing in your primary language, this creates a real tension. For businesses here, it mostly does not, because English is already your primary language for professional content.

This sounds like an advantage. It is, but only if you understand what "English-first" actually requires in an AI search context, which is more specific than just "writing in English."

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What Fan-Out Means and Why It Matters

When you type a question into ChatGPT, you are not getting a single retrieved document. Behind the scenes, ChatGPT runs multiple background searches to gather information from multiple sources, synthesises them, and presents an answer. These background retrievals are called fan-out queries.

Peec AI's research found that AI systems disproportionately pull from English content during this fan-out phase. The reason is structural: most of the open web's authoritative, well-structured, linkable content is in English. Wikipedia, academic papers, industry publications, major news sources: the training data that LLMs were built on skews heavily English, and the retrieval habits they developed reflect this.

For a business in France or Japan or Brazil, this creates a real problem. The content you write for your local audience may not be the content AI systems reach for when answering English-language fan-out queries, even if you serve an international market.

For an SME writing professional content in English, the structural barrier is already cleared. The question is whether your content is being reached and cited, or being overlooked in favour of more clearly structured sources.

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The Advantage Singapore Businesses Often Do Not Press

Singapore's business landscape is natively English-language in a way that is genuinely unusual for a non-English-speaking country by origin. Our business communications are English. Our government websites, regulatory guidelines, professional associations, and major media are English-language. Most SME websites, blogs, and marketing materials are written in English.

This puts local businesses in a rare category: locally relevant, locally specific, and in the language AI systems prefer to retrieve from.

Here is the part that most businesses miss: proximity to English-language content is not the same as AI-citable content.

A lot of SME content is English on the surface but vague in structure. It describes services in broad terms, avoids making specific claims, uses no structured data, has no clear authorship, and says nothing that an AI system could confidently excerpt and attribute.

Being in the right language is the baseline. Being in the right format with the right specificity is what actually gets you cited.

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What AI-Citable English Content Actually Looks Like

There are a few qualities that separate content AI systems are likely to retrieve and cite from content they pass over.

Specific, verifiable claims over vague assertions

"We help businesses grow" is not citable. "Singapore SMEs that invest in SEO within their first two years of operation see meaningfully higher organic traffic than those that rely solely on paid channels" is citable, assuming it is sourced or attributed.

AI systems are pulling from content to answer specific questions. They need content that makes specific claims, ideally with attribution. Every time you write a vague generality when you could write a specific, sourced claim, you are giving AI retrieval systems less to work with.

This does not mean padding your content with invented statistics. It means writing with precision: specific processes, specific outcomes, specific local context. "Our clients in the healthcare sector typically see enquiry volume increase meaningfully in the first three months after a website redesign" is more useful to an AI system than "our clients get great results."

Structured for quick extraction

AI citation systems scan content in a similar way to how human researchers skim a well-organised report. Clear headers, short paragraphs, and a logical flow that answers one question before moving to the next.

If you have content that buries the answer to a specific question in the middle of a long general paragraph, AI systems are less likely to extract it cleanly. Write as if someone is going to pull one paragraph from your article and use it to answer a question. Which paragraph would you want them to pull? Make sure that paragraph is findable.

Clear authorship and institutional credibility

Peec AI's research, and parallel findings from SparkToro on AI visibility tracking, both point to the same pattern: AI systems are not content-agnostic. They weight sources by credibility, and credibility signals include authorship, institutional affiliation, and consistency of publishing.

For an SME, this means a few practical things: your content should have a named author or a credible company byline. Your company page should explain clearly who you are, what you do, and why you are qualified to say what you are saying. Publishing consistently, even at a modest cadence, builds the pattern that AI systems recognise as a credible source rather than a one-off publisher.

FAQ and Q&A structure

This is not new advice, but it is more important in the AI search era than it was before. Content structured as questions and answers maps directly to how fan-out retrieval works: AI systems are looking for content that matches the specific sub-questions they are trying to answer.

If you run a digital marketing agency in Singapore, a page that directly answers "how much does SEO cost in Singapore," "how long does SEO take to work in Singapore," and "what should I look for in a Singapore SEO agency" is more citable than a page that vaguely discusses your SEO services without addressing these questions directly.

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The SparkToro Caveat: Do Not Build a Strategy Around Tracking This

A note of honesty before you make AI citation visibility a new KPI.

SparkToro recently published research on the AI visibility tracking industry, and the finding is worth quoting: AI systems are highly inconsistent when recommending brands or products. The same query asked to the same AI system on different days can produce different sources and different citations. Tracking AI visibility across the industry, reportedly a $100 million-plus market, is producing inconsistent results because the underlying systems behave inconsistently.

The implication: do not start paying for AI visibility tracking tools or spending serious time obsessing over whether you appeared in a specific ChatGPT answer on a specific day.

What this research does not change is the underlying logic. High-quality, structured, English-language, specifically-attributed content is more likely to be cited by AI systems than vague, unstructured content in other languages. The directional insight from the Peec AI research is sound. The idea of tracking your "AI visibility score" week-to-week as a KPI is less so.

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What SMEs Should Actually Do

Four things, in order of priority:

1. Audit your content for specificity

Go through your five most important pages, whether that is your service pages, your blog posts, or your about page. Ask yourself: if an AI system pulled one paragraph from this page to answer a specific question, would that paragraph be useful? Does it make a specific claim, describe a specific process, or address a specific concern?

If the answer is mostly no, that is your content strategy for the next quarter.

2. Add FAQ schema to your key pages

This is technical but not complicated. FAQ schema tells AI systems (and Google) that your page contains direct answers to specific questions. It makes your content easier to extract and attribute. Your web developer can implement this, or platforms like WordPress have plugins that handle it without custom code.

For businesses in regulated industries, healthcare, legal, financial services, education, FAQs that address specific Singapore-context questions (compliance, grants, regulations) are particularly valuable. They are the questions Singaporeans are asking that generic international content cannot answer.

3. Write about Singapore-specific topics in depth

One of the genuine advantages Singapore businesses have is the ability to write authoritative, locally specific content that international competitors cannot replicate. "How PDPA affects your email marketing programme" is a topic only Singapore-context publishers can address credibly. "How to apply for the PSG grant for a website redesign" is the same.

AI systems retrieving content for Singapore-specific queries will disproportionately favour Singapore-specific sources for Singapore-specific topics. This is the intersection where your English-language advantage and your local knowledge compound.

4. Build consistent publishing into your process

One good article every six months does not establish you as a credible, consistent source. A modest, consistent cadence, whether that is two pieces per month or four, builds the publishing pattern that credibility signals depend on. Done is better than perfect. Specific is better than comprehensive. Consistent is better than occasional.

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If you want to build a content strategy that positions your business for both traditional search and AI-driven discovery, Magnified works with SMEs on content and SEO strategy. We will give you a practical view of what is worth doing and what is noise.

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The Peec AI research referenced in this article was published in early 2026 and covers an analysis of millions of ChatGPT prompts. SparkToro AI visibility tracking research was published in the same period. Both findings represent snapshots of AI system behaviour at the time of publication; AI search systems continue to evolve rapidly.