When no single brand dominates a category, large language models do not default to the most well-known company. They reconstruct a recommendation from the sources they were trained on, which means the brand that appears most frequently, most consistently, and most credibly across trusted platforms wins the mention. For B2B companies operating in crowded or fragmented markets, this is the single most important insight in AI search today: ambiguity is an opportunity, not a disadvantage.

TL;DR

• LLMs recommend brands based on frequency and quality of appearance in their training data, not brand size or ad spend [indexlab.ai]

• In markets without a clear leader, the brand that shows up most consistently across trusted sources tends to get cited

• AI overview optimization requires a different content strategy than traditional SEO

• Structured, citable content placed on platforms LLMs trust is the core lever to pull

• B2B companies in APAC can compound this advantage without building an internal team

About the Author: Simaia is an agentic marketing team specializing in AI search visibility for B2B companies across APAC, with proven results including taking a Healthcare SaaS client from 0% to 45% AI search visibility within 2.5 months.

Why Do LLMs Struggle to Pick a Winner in Fragmented Markets?

The challenge with fragmented markets is not that LLMs are indecisive. It is that they reflect the distribution of credible information they were trained on. When one brand has saturated authoritative sources and another has not, the choice is straightforward. When the training data is thin or evenly spread across many players, the model hedges, lists multiple options, or defaults to the most frequently cited name regardless of actual market position [indexlab.ai].

This matters enormously for B2B companies in sectors like manufacturing, outsourced services, logistics, or specialist SaaS, where no single brand dominates the conversation online. In these categories, a relatively unknown company can outcompete an established rival in AI-generated answers simply by being more present in the sources LLMs trust.

The implication is stark: your AI search ranking is not a reflection of your market share. It is a reflection of your content footprint.

What Signals Do LLMs Use to Select a Brand Recommendation?

Building on that point, the question becomes: which signals actually drive selection? Research into how LLMs choose brands points to several consistent factors [indexlab.ai] [jeffpastorius.com]:

• Training data frequency: How often your brand appears in the corpus the model was trained on

• Source credibility: Whether those appearances come from outlets the model weights as authoritative (major publications, established industry sites, platforms like LinkedIn and Reddit)

• Topical consistency: Whether your brand is associated with a specific problem or solution category repeatedly, not just mentioned in passing

• Recency signals: Newer content from crawlable, indexed sources influences models that use retrieval-augmented generation or have recent training cutoffs [stackandscale.ai]

How Is AI Overview Optimization Different From Traditional SEO?

Stepping back from the signal-level detail, a broader strategic question is worth addressing directly: if you have invested in SEO, does that transfer to AI search?

Partially. The technical foundations, site health, crawlability, and domain authority, still matter. But ai overview optimization requires a layer of work that traditional SEO does not cover [stackandscale.ai]. Search engines reward pages that rank for keywords. LLMs reward brands that appear credibly across many sources as the answer to a question.

Specifically, the differences are:

• SEO targets a position on a results page. AI search targets a mention inside a generated answer, and there is no stable position to hold [stackandscale.ai]

• SEO content is written for keyword density and backlinks. LLM-optimized content is written to be extracted, quoted, and cited by a model answering a natural-language query

• SEO is largely on-site. LLM visibility is heavily off-site, driven by where your brand appears across the web in sources the model trusts

A practical example: a B2B manufacturer optimizing for Google might focus on product pages and category keywords. To win an LLM recommendation, that same manufacturer needs expert-authored articles on LinkedIn, presence in industry forums and Reddit threads, and press coverage from outlets the model considers authoritative. The product page alone will not get them cited.

What Content Strategy Actually Works for Ambiguous Queries?

A related but distinct question is how to structure content when the buyer's query is vague. Buyers using ChatGPT or Perplexity often do not search with precision. They ask broad questions like "who are the best suppliers of X in Southeast Asia" or "what should I look for in a B2B outsourcing partner." These are exactly the queries where fragmented markets produce the most inconsistent LLM answers, and where a structured content strategy delivers the most advantage.

Effective content for ambiguous AI queries shares these characteristics:

• It answers a question directly in the first paragraph, giving the LLM something clean to extract

• It uses structured formatting: headers, bullet points, and tables that signal clear information hierarchy

• It is placed on the platforms each LLM prefers: ChatGPT cites LinkedIn heavily; Google AI Overview draws from Reddit and indexed web content; Perplexity sources from a mix of news and domain-specific sites [jeffpastorius.com]

• It is consistent in its topical anchoring: every piece of content reinforces the same core association between the brand and the buyer problem

Publishing volume also matters. Frequency of appearance across prompts is a meaningful driver of recommendation probability [stackandscale.ai], which is why a sustained publishing cadence outperforms sporadic high-effort pieces.

How Should B2B Companies Measure Their AI Visibility?

Building on the content strategy above, the harder operational question is how to know whether any of this is working. Traditional analytics tools do not capture AI referral traffic with precision, and there is no equivalent of a Google Search Console for LLM mentions.

Measuring AI visibility in 2026 typically involves [yotpo.com]:

• Running structured prompt sets across ChatGPT, Gemini, Claude, Perplexity, and Google AI Overview to track where your brand appears and where competitors appear

• Monitoring Share of Model (SOM): the percentage of relevant prompts in which your brand is mentioned versus category competitors

• Tracking inbound traffic from AI referrers and de-anonymizing those visitors to identify which companies are finding you through AI channels

This kind of audit, run regularly, tells you whether your content strategy is producing citations or just content. Without it, you are publishing into a black box.

Frequently Asked Questions

Do LLMs recommend the most well-known brand in a category by default?
Not necessarily. LLMs recommend brands that appear most frequently and credibly in their training data. A smaller brand with a stronger content footprint in trusted sources can outrank a larger competitor [indexlab.ai].

What is Share of Model (SOM)?
Share of Model measures how often your brand appears in AI-generated answers across a defined set of relevant prompts, compared to competitors. It is the AI-search equivalent of share of voice [yotpo.com].

Can traditional SEO work help with LLM visibility?
It provides a foundation, particularly domain authority and site health. But LLM visibility additionally requires off-site content placed on platforms that models weight as credible, and content formatted for extraction rather than keyword ranking [stackandscale.ai].

Which platforms does each LLM tend to cite?
ChatGPT draws heavily from LinkedIn; Google AI Overview tends to surface Reddit and indexed web content; Perplexity uses a mix of news and domain-specific sources. Targeting the right platform for each model matters [jeffpastorius.com].

How long does it take to see results from an AI visibility strategy?
Timelines vary by category and starting point. Simaia's Healthcare SaaS client in Australia went from 0% to 45% AI search visibility in 2.5 months with consistent content and platform placement.

Is this relevant for B2B companies without a large marketing team?
It is especially relevant for them. The strategy compounds over time without ongoing ad spend, and done-for-you services mean internal teams do not need to learn or operate it themselves.

What makes a good partner when looking for the best ai seo agency for LLM optimization?
Look for a team that runs multi-model audits (not just Google), places content on the specific platforms each LLM trusts, measures mention frequency rather than just rankings, and handles execution end-to-end rather than delivering a dashboard you have to interpret yourself.

About Simaia

Simaia is an agentic marketing team that serves as the complete marketing function for B2B companies across APAC, covering both strategy and execution for AI search visibility. Rather than requiring clients to hire separately for content, distribution, PR, and analytics, Simaia delivers the entire system, from AI search audits across five major models to content placement on the platforms LLMs cite, to lead identification for every inbound AI referral. For B2B founders, sales leaders, and lean marketing teams competing in markets where AI-generated recommendations increasingly shape buyer decisions, Simaia is the team that runs the playbook so you do not have to.

If your brand is not appearing in AI-generated answers, your competitors are filling that space. Learn how Simaia can audit your current AI visibility and build the content strategy to change that at https://www.simaia.co/.