“What’s our vector, Victor?”

What was once a punchline in ‘Airplane!’ can actually be a useful marketing phrase now, assuming you work with someone named Victor. 

Jokes aside, we’re referring to vector search, which is shaping up to be as influential to the search marketing world in 2025 as PageRank was back in 1999. 

Vector search is one of the primary driving forces behind generative AI platforms like ChatGPT and Perplexity. 

It works by using machine learning to convert text into vectors, which are numerical representations of word meanings. Instead of matching keywords lexically, vector search finds related words by measuring the distance between their vectors. 

In a nutshell, this is how AI search tools are able to identify content that’s conceptually similar to a user’s prompt, even if it doesn’t contain any exact-match keywords. 

Since vector search is so important, marketers need to understand things like embeddings, semantic similarity, and AI-driven retrieval just as they would backlinks back in the early 2000s. 

In this guide, we’ll unpack how vector search works, including how you can optimize for it. 

Recap: How PageRank Changed Search Forever, and How it Parallels Vector Search

First, let’s briefly look at how the PageRank system revolutionized online search back in 1999. 

Before Google and the PageRank algorithm entered the scene in late 1998, search engines were extremely basic in how they ranked content. 

It was entirely keyword-based, and the system was entirely lexical, not semantic. 

Translation?

Keyword stuffing worked like a charm. 

If a website wanted to rank #1 on Yahoo or AltaVista, all they had to do was cram exact-match keywords in their content. The more keywords they stuffed into a piece, the higher they’d rank. 

The PageRank algorithm was revolutionary because it provided a way to measure a website’s trustworthiness without requiring a massive investment in new infrastructure. 

Instead, PageRank used the link graph as its foundation, which is a representation of all the hyperlinked websites online. The system worked by assigning ‘trust signals’ to hyperlinks, with links coming from credible domains carrying more weight. 

In a nutshell, SEO grew from keyword stuffing to link-building virtually overnight. Any website that failed to adapt simply disappeared from the search results. 

This parallels the modern transition to vector search. 

Brands that don’t adapt to GEO (generative engine optimization) will see their online visibility drop, while brands that embrace it will expand their influence. 

What Vector Search Is (and How it’s Different from SEO)

LLM-powered search tools like ChatGPT and Perplexity rely on vector databases and embeddings to interpret intent and make connections between similar topics, even when the phrasing differs. 

This is done by assigning mathematical values to word meanings, which enables AI tools to make countless semantic connections.  

As a result, lexical keyword matching is quickly becoming a thing of the past. 

If your content is conceptually aligned with the topics your audience looks for online, even loosely, it’s fair game for LLMs to surface and cite. 

For example, if a user asks ChatGPT something like, “How does power of attorney work?” it may cite content from an estate planning website with strong topical authority, even if they don’t have a blog post using those exact words. 

If they have content containing words and topics like legal authorization, decision-making on someone’s behalf, or financial and medical authority, vector search recognizes the semantic overlap.

How vector search enables semantic range 

Since vector embeddings capture the meaning behind words, a tool like ChatGPT can understand that the content is related to the user’s question.

That means brands no longer need to obsess over keyword research and use exact-match keywords in their content.

In the past, if you didn’t optimize for the right keywords, you didn’t stand a chance at reaching your audience online. 

With vector search, online visibility is less restrained, which is actually a very good thing for marketers. 

Here’s what we mean. 

We’ve already established how vector embeddings allow semantic connections. 

Well, whenever you establish authority and trust on a particular topic with LLMs, you’ll qualify for a vast semantic range of relevant keywords. 

If a member of your target audience prompts a tool like ChatGPT with any words related to your area of expertise, your brand may appear as a citation (or the tool may even recommend your products directly). 

Thus, marketers no longer need to painstakingly optimize for one keyword at a time. 

Instead, establishing strong topical authority on a particular subject with generative AI platforms can scale your brand’s reach infinitely better than traditional keyword-based SEO. 

Here’s a breakdown of the primary differences between vector search and PageRank SEO:

How to Optimize for Vector Search

As we’ve established, vector search operates on a whole different set of parameters than PageRank SEO. 

That’s why a new marketing methodology is required to optimize for it. 

GEO focuses on optimizing online content for generative AI platforms that use vector search and entity recognition. 

It does away with legacy SEO tactics in favor of entity associations, editorial quality, and semantic relevance. 

This can be a stark transition for marketers who have engaged in traditional SEO for decades up to this point. Yet, it’s undeniable that GEO marks the way of the future for search marketing. 

Even if you’re still seeing positive results from SEO right now, you should familiarize yourself with the fundamentals of GEO to prepare for the future. 

Here are the top ways to optimize your content for vector search models and generative AI platforms. 

Strengthen entity associations 

LLMs use entity recognition and linking to:

1. Identify and disambiguate people, places, organizations, and other entities 
2. Uncover the connections between concepts, brands, and competitors (through vectors) 

Because of this, a major aspect of GEO is optimizing your entity profile. 

You want LLMs to know that you’re a trusted authority figure in your areas of expertise. 

Here’s how you can do just that:

• Consistent topic coverage with content clustering – Creating content clusters is the best way to establish topical authority. You should develop interlinked clusters that cover a broad set of interrelated topics related to a particular subject or niche. The presence of internal links reinforces the connection between each piece. 

Content clustering is also how you can qualify for a vast semantic range of concepts and keywords. If your primary topic is electric vehicles, you should create content for related topics like battery range, charging infrastructure, and government incentives. This will help vector models recognize the semantic connections. 

• Optimize for semantic intent – Try to create content for the semantic intent behind popular user queries and prompts without focusing on individual keywords. This will make it easier to cover related topics and answer similar questions without veering off topic. 

For instance, consider this lengthy prompt, “Create a guide on how small businesses can introduce sustainable packing solutions while remaining cost-effective.”

The intent here is informational and commercial. 

Understanding this can breed several content ideas, such as a general overview of sustainable packing, a cost analysis piece, real-world case studies, and an environmental benefits listicle. 

• Build strong entity relationships – You should link your content to major knowledge databases like Wikidata or Crunchbase. It’s also crucial to back up your claims with links to authoritative external sources. Doing this creates a network of trusted connections that vector search models will easily discover. 

Use structured data for contextual clarity 

Structured data like semantic HTML and schema markup makes your content easier for LLMs to parse, categorize, and cite.

While embeddings don’t rely directly on schema to form semantic connections, structured data helps AI systems understand context and intent more precisely, which increases the likelihood that your content will get matched with relevant queries. 

In particular, you should use markup from Schema.org to clearly define reviews, FAQs, entities, relationships, authors, and other forms of key data. 

Build semantically relevant backlinks and brand mentions 

Brand mentions and semantically relevant backlinks are two extremely significant trust signals for AI search platforms. 

Semantic relevance is key here, as LLMs don’t pay attention to static authority scores like Domain Authority and Domain Rating. 

In short, AI models want to know that:

• Trusted websites mention your brand and link to your website 
• Related websites recommend your products and services 
• The general sentiment surrounding your brand is positive 
• Other websites in your field view you as an authority figure 

In GEO, backlink quality is everything, so don’t bother with building a large quantity of medium or low-quality backlinks. 

While they had a compounding effect in the PageRank system, they won’t do anything to improve your authority on AI search tools. 

Concluding Thoughts: Vector Search is the New PageRank

By 2030, it’s highly likely that PageRank SEO will feel as outdated as keyword stuffing does today. 

Vector search has made generative AI systems more adept at identifying related topics and semantic connections, so the days of keyword matching are all but over. 

The best thing businesses can do right now is adopt GEO as soon as possible. 

Early adopters who optimize for vector discoverability will earn a serious edge over the competition and will define the next era of SEO. 

Are you ready to embrace GEO and outfox your competitors?

Don’t wait to book a strategy call with our team of GEO experts.