How LLMs Decide Which SaaS Companies to Recommend (and How to Become One They Cite)

When someone asks ChatGPT or Perplexity to recommend a tool, the models surface companies with clear, consistent, well-sourced positioning. They do not surface companies that are described the same way as twenty competitors. If your brand is generic, an LLM cannot distinguish you from the noise. This is not an SEO problem. It is a positioning problem.

What changed, and why it matters to you

Search used to be about showing up in a list. Someone typed a query, ten blue links appeared, and they clicked.

Now, a meaningful share of that behavior has shifted. Founders and buyers ask AI assistants for tool recommendations directly. ChatGPT, Perplexity, Gemini, and Claude synthesize answers from what they know about the market. If they know you, they mention you. If they do not, you do not exist in that answer, and you will never know the deal you lost.

That is the new version of "deals you'll never know you lost." It is not a single lost call. It is invisible attrition across every buyer who asked an AI before they ever visited your site.

SEO, AEO, and GEO are not the same thing

These three terms get conflated constantly. They are not synonyms. They describe different optimization targets.

SEO is still table stakes. AEO is about structured, extractable answers. GEO is the newest layer, and it requires something different from both: not just discoverability, but credibility that a model can verify from multiple sources.

You need all three. But right now, most B2B SaaS brands are underinvesting in GEO because they do not yet understand what it actually requires.

The real input: brand legibility

Here is what the content marketing industry gets wrong about GEO. Most articles about "ranking in AI search" are lists of technical tactics. Add schema markup. Use FAQ pages. Optimize for featured snippets. Build backlinks.

Those things help. But they are not the root problem for most B2B SaaS companies.

The root problem is that your brand is not legible. An LLM cannot form a confident, citable opinion about you because everything you say about yourself sounds like everything your competitors say about themselves.

If your positioning is "the AI-powered platform that helps teams do more with less," you share that sentence, essentially, with a few thousand other companies. A model trained on the internet has absorbed all of those descriptions. When a user asks for a recommendation, the model cannot confidently surface you because there is no meaningful signal to distinguish you.

Brand legibility means: when a model encounters your company name across review sites, case studies, third-party write-ups, and your own content, it encounters a consistent and specific description of who you are and what you do differently. The specificity is what lets the model make a confident attribution.

Four markers of a legible brand:

1. A specific claim, not a category claim. "Compliance automation for Series A fintech companies" is legible. "The modern compliance platform" is not.
2. Consistent language across external sources. Your G2 profile, Capterra listing, press mentions, and customer testimonials all use similar language to describe what you do.
3. Third-party corroboration. Reviews, case studies, earned press, and integration directory listings confirm the claim independently.
4. Named use cases. The model needs to know not just that you exist but what you are for. Concrete use cases anchor the recommendation.

What GEO-optimized content actually looks like

This is where it gets practical.

Structure answers, not just articles. Every major piece of content should open with a self-contained answer to the question it addresses. Lead with the summary. Support it below. This is what makes content extractable by a model. Walls of prose with the answer buried in paragraph seven do not get cited.

Use specific, factual language. Declarative sentences. Numbers when you have them. Named customer types. Avoid vague intensifiers ("robust," "best-in-class," "powerful") that carry no information.

Build third-party surface area. Review sites, integration directories, partner pages, industry lists, and earned press coverage all function as independent signals that a model can triangulate. One strong source is not enough. The model needs to encounter you from multiple angles.

Publish in your lane, consistently. A company with two blog posts and a dead LinkedIn page looks like a company that might not exist anymore. Consistent output across your own channels signals that the company is active and that claims can be verified.

Create citable definitions. If you want to own a concept, define it explicitly. This post defines GEO in a way that can be extracted and attributed. That is intentional.

The positioning work comes first

None of the content tactics above work if the positioning underneath them is soft.

If you do not have a specific, defensible claim about what makes you different and who you are for, no amount of schema markup or FAQ pages will fix it. The model will encounter vague, generic content from every source it checks, and it will reflect that vagueness back.

This is why GEO is a brand problem before it is a content problem. The work is to get clear on what you are, build consistent language around it, and then systematically publish that language across every surface a model might encounter.

That is also the work most B2B SaaS founders at the $2M to $15M mark have not done yet. They have a product. They have customers. They have a pitch deck. What they do not have is a brand that is legible to a market, or to a model.

Frequently asked questions

What is GEO (generative engine optimization)?

Generative engine optimization (GEO) is the practice of structuring your brand, content, and online presence so that AI language models (such as ChatGPT, Perplexity, Claude, and Gemini) can accurately identify, describe, and recommend your company in response to relevant queries. Unlike SEO, which targets search engine rankings, GEO focuses on the signals that influence which companies an AI cites or surfaces in a synthesized response.

What's the difference between SEO, AEO, and GEO?

SEO (search engine optimization) optimizes for ranking in traditional search results. AEO (answer engine optimization) optimizes for being pulled into direct answers and featured snippets, such as Google's AI Overviews or voice assistant responses. GEO (generative engine optimization) optimizes for being recommended or cited by large language models in conversational AI tools. All three matter, but they require different inputs: SEO needs authority and relevance signals; AEO needs structured, extractable answers; GEO needs brand legibility, specificity, and third-party corroboration across the web.

How do LLMs decide which brands to recommend?

Large language models surface companies based on what they have learned from large bodies of text. When a model encounters consistent, specific, third-party-corroborated descriptions of a company across multiple sources, it can form a confident attribution. When a company's positioning is vague, generic, or contradicted by silence (no reviews, no press, no consistent language), the model either cannot distinguish it from competitors or does not surface it at all. The inputs that matter most are: specificity of your positioning, consistency of language across external sources, volume and quality of third-party mentions, and clearly structured content that can be extracted and cited.

How do I get ChatGPT or Perplexity to recommend my product?

Start with your positioning. If your brand is generic, no content tactic will fix it. Then: (1) Publish clear, structured content that opens with direct answers to the questions your buyers ask. (2) Build third-party surface area: earn reviews on G2 and Capterra, get listed in integration directories, pursue relevant press coverage. (3) Use specific language consistently across every channel, your own and external. (4) Define the concepts and use cases you want to own, explicitly and in plain language. (5) Publish consistently so models see an active, credible company. There is no shortcut. The work is positioning first, then content at scale.

Can you pay to be recommended by an LLM?

No. There is no paid placement inside ChatGPT, Perplexity, Claude, or Gemini's generative responses. These models do not sell recommendations. The outputs are synthesized from training data and retrieval, not from advertising inventory. This is a fundamental structural difference from Google Search, where paid ads occupy prominent positions. An LLM recommends you because it has encountered credible, consistent, corroborated information about you, not because you paid for the slot. Anyone selling "guaranteed LLM visibility" through a paid program is misrepresenting how these systems work. The implication is significant: the only durable path to AI recommendation is a brand that is genuinely legible and well-sourced across the web.

Your brand should be as good as your product

If your product is solid and you are still invisible to AI assistants, the gap is your brand.

Not your ad budget. Not your schema tags. Your brand. Specifically: how clearly and consistently you can describe what you do, who you do it for, and why that matters, across every surface a model might check.

That is the work Sunny Side Up does with growth-stage B2B SaaS founders. We function as the embedded creative team you do not have to hire, and we build brands that are legible to people and to models.