There's a technical concept quietly determining whether an AI recommends your brand or your competitor's. Almost no one in marketing has heard it explained in a way that actually means something. It's called an embedding — and the intuition behind it is simpler than you'd expect.
The Problem Embeddings Solve
Computers don't understand words. They understand numbers. So for an LLM to work with language, it needs to convert text into something that can be compared, calculated, and approximated.
That's exactly what an embedding does: it turns text into a list of numbers that represents the meaning of that text. Not the spelling of a word — the idea it carries, the context it lives in, the concepts it connects to.
Think of it this way: if you could plot every concept in the world onto an imaginary multi-dimensional map, similar concepts would cluster together. "Investment" and "portfolio" would be neighbors. "Antivirus" and "cybersecurity" would share the same district. "Espresso" and "coffee shop" would be a short walk apart.
That imaginary map is where embeddings live. The distance between two points represents how semantically similar two pieces of text are — not whether they share the same words, but whether they carry the same meaning.
Why This Matters for Brand Visibility
When someone types "what's the best project management software?" into ChatGPT, the model doesn't run a keyword search. It looks for content that's semantically close to the intent behind that question — and it decides what to include based on meaning, not vocabulary match.
If the content your company has published about project management is vague, generic, or doesn't signal genuine expertise, it may sit far away from the queries your potential customers are asking. Far away semantically means a lower chance of being cited.
The reverse holds too: content that directly addresses real questions in your field, uses the language specialists actually use, and stakes out clear positions tends to produce embeddings closer to the relevant queries. That proximity increases your odds of being surfaced.
What This Has to Do with Training Data
LLMs build their embeddings during training, based on the texts they consumed. If your company has never been mentioned in authoritative sources — specialized publications, industry wikis, respected forums, press coverage — you may simply not exist in the semantic space the model constructed.
Here's the part most people underestimate: it's not enough to exist in training data. You need to exist near the right concepts. A passing mention in a tool roundup doesn't place your brand in the same semantic neighborhood as a deep article about the problem you actually solve.
That's why where you appear and in what context matters as much as how often.
Real-Time Retrieval: Embeddings Work at Query Time Too
Models like Gemini and ChatGPT with Search enabled don't rely only on static training. They also run live retrieval and use embeddings to rank which results are most relevant to a user's query.
In this context, the semantic quality of your pages remains the selection factor. A well-structured page that uses precise language and directly answers real questions in your field will produce embeddings closer to the relevant queries — and a better shot at being pulled as a source.
What You Can Actually Do With This
There's no way to "hack" embeddings the way people once hacked meta keywords. That's actually good news: this game is semantic, not a surface-level technical trick. Which means genuine content quality matters in a way it never quite did before.
A few practical implications:
Be specific. Content that names concrete problems, uses industry-standard vocabulary, and answers real questions is semantically closer to what your audience actually asks. Generic content about "everything" is close to nothing.
Write for questions, not keywords. LLMs process intent. Content that anticipates and fully answers a question ("how do I compare tools for X?") has a more precise embedding than content that just includes search terms.
Authority sources multiply the effect. A qualified mention in a respected publication contributes more to your brand's semantic footprint than ten internal blog posts repeating the same concepts in the same words.
Positioning consistency helps. If your brand appears associated with the same cluster of concepts across your site, press coverage, reviews, and forums, it builds a denser, more coherent semantic signature.
You Don't Need the Math to Use the Logic
Embeddings are, technically, vectors in high-dimensional space. But for marketers, what matters is the implication: AI operates by proximity of meaning, not keyword matching.
What puts your brand close to the concepts your customers search for? Specialized content, precise language, presence in respected sources, and clarity of positioning. None of this is new — but it's never been as directly determinative of where you appear in AI responses.
If you want to understand where your brand sits in the semantic space of each major LLM — and where it's invisible — tools like Genoma let you map exactly that: which queries in your industry surface you, which ones ignore you, and what the pattern reveals about your current AI presence.