Modern Google Search Is Written in Numbers: A Marketer’s Guide to Vector Search

Caleb Turner on May 24, 2025

Introduction: Why Your Keywords Are Losing Their Super-Power

If you still measure SEO success by how many times you can squeeze “best running shoes” into a paragraph—stop the treadmill. Google is no longer looking for an exact text match; it’s looking for a conceptual match. Behind the scenes, the engine turns every query and every document into long lists of numbers called vector embeddings and then asks an algorithm named ScaNN to find the closest pairs. In this numeric universe, “heart attack symptoms” finds its soul mate in “signs of myocardial infarction,” even though not a single word overlaps.

1. From Keywords to Meaning

Back in the dial-up days, ranking was glorified pattern matching: say “blue widgets” five times, win a medal. Vector search re-labels the task as meaning matching. It encodes queries and pages into multidimensional vectors where geometric distance = conceptual similarity. That’s why conversational queries like “my phone got wet and won’t turn on—help!” can surface posts titled “reviving a water-damaged smartphone” even though you never typed the word “reviving.”

Why that matters

Broader questions answered. Google can safely jump from slang to scientific jargon without scaring the user.
SEO shifts focus. You now optimise for topical depth and context, not just a single two-word phrase.

2. What Exactly Is an Embedding?

An embedding is a vector—hundreds or thousands of floating-point numbers—that acts like a GPS coordinate for ideas. Two embeddings that point in almost the same direction signal “these pieces of content are basically talking about the same thing.”

Creating those vectors once required PhDs and GPU farms; today a single API call or drag-and-drop notebook in Vertex AI spits them out faster than your intern can ask, “Do we charge extra for semantic optimisation?”

3. ScaNN—Google’s “Find-the-Needle” Algorithm

Once everything is a number, you still need to locate the nearest neighbors in a haystack of billions. Enter ScaNN (Scalable Nearest Neighbors)—Google’s open-sourced speed demon that performs that lookup in milliseconds.

In 2024 Google released SOAR, a tune-up that adds clever redundancy so ScaNN can run even faster and cheaper without blowing out your cloud budget—handy when your product catalogue is larger than a Netflix binge list.

4. How Vertex AI Uses ScaNN

Inside Google Cloud, Vertex AI Vector Search (sometimes still called “Matching Engine”) stores your embeddings, builds an index, and quietly delegates the “find the closest vectors” chore to ScaNN.

Marketers can already play: upload a product feed, ask Vertex AI to embed the titles and descriptions, and voilà—“shoes like this one” recommendations appear without writing any C++ or sacrificing any goats to the ML gods.

5. AI Overviews and the “Query Fan-Out” Party Trick

Patents titled “Generative Summaries for Search Results” describe a workflow where Google splinters your single question into a dozen smart sub-queries, fetches the best passages via vector search, and lets Gemini compose the final paragraph you now know as an AI Overview (AIO).

Because ScaNN already runs in the same infrastructure, many experts assume the identical stack powers AIO—no official badge from Google yet, but the puzzle pieces line up like a well-optimised internal-link structure.

6. vec2vec—One Vector Space to Rule Them All?

Researchers from Stanford and DeepMind introduced vec2vec, a pint-sized neural net that can translate embeddings from one model’s “language” (say, open-source BERT) into another’s (say, Google’s Gemini) without paired data. If it holds up, you could generate vectors with a free model, convert them, and still rank in Google—saving API tokens for more important things, like Friday coffee.

7. Do You Need to Be a Coder?

Conceptual level (no code): Know that short distance in vector space means “these two texts are buddies.” That alone improves how you design content clusters.
Low-code level: Use cloud UIs, Zapier, or a Google Sheet add-on to fetch embeddings and store them. Your résumé still reads marketer, not engineer.
Full-code level: Dive into Python scripts to fine-tune models, tweak ScaNN hyper-parameters, or self-host FAISS if you enjoy living dangerously.

Most SEOs only need level one and two; level three is for people who think “Friday night” and “CUDA kernel” belong in the same sentence. (No judgment… okay, maybe a tiny bit.)

8. What This Means for SEO & Content Strategy

Go deep, not wide. Cover your topic so comprehensively that the vector space around it looks like downtown Manhattan at rush hour—crowded with your content.
Write like a human. Semantic models adore clarity and punish keyword salad.
Structure for sub-queries. Use logical headings, FAQs, and schema so Google’s fan-out routine has plenty of passage candidates.
Watch the tools. Vertex AI’s public dashboards give early hints of how Google “sees” your page numerically; treat it like a free MRI for content health.

9. Key Takeaways (Pin These to Your Virtual Fridge)

Vector search turns content into numbers and finds meaning through math.
ScaNN is Google’s rocket engine for that math and likely sits under AI Overviews.
SOAR makes ScaNN faster; vec2vec might make it universal.
You don’t need a CS degree—just curiosity and the courage to let go of keyword crutches.

With that foundation, your SEO playbook is officially ready for the semantic era. Now excuse me while I go translate this conclusion into a 1,536-dimension vector—apparently that’s how the cool kids say goodbye.

The Future Is Semantic: Why Vector Embeddings Will Re-Write Your SEO Playbook

Caleb Turner on May 23, 2025

From Keyword Tweaks to Content Engineering

Remember when SEO success meant sprinkling the right keywords in title tags and praying for backlinks? That era is fading fast. Google’s AI Mode and its expanded AI Overviews now synthesize answers directly in the SERP, citing passages—often buried deep inside a site—rather than the traditional homepage snippets. In fact, 82 percent of citations in AI Overviews point to pages tucked two or more clicks away from the front door.

If Google is willing to dig that far beneath the fold, it’s clearly valuing topic depth and semantic relevance over surface-level keyword placement. Welcome to the age of Relevance Engineering—the discipline that treats visibility as a measurable engineering challenge instead of an “optimization” afterthought.

Why Semantic Optimization Matters

Search Queries Are Now Semantics, Not Strings

Google’s 2013 Hummingbird overhaul replaced purely lexical (word-matching) scoring with semantic understanding—essentially asking, “What does the query mean?” rather than “Which words appear?” That shift only intensified with every language-model upgrade since.

Generative AI Needs Precise Context

Large language models (LLMs) like Gemini 2.5 or GPT-4 break user prompts into sub-queries, retrieve semantically similar passages, and stitch them into coherent answers. If your content isn’t structured for easy extraction—think tight paragraphs, clear headings, and complete subject-verb-object statements—AI may skip you in favor of a competitor who writes with vectors in mind.

Behavioral Metrics Still Close the Loop

Click-through rates, dwell time, and “pogostick” abandonment remain crucial. But they’re now the second filter. First, you must be retrieved from vector space; only then can engagement metrics prove you deserve to stay visible.

Vector Embeddings 101: Coordinates for Meaning

A vector embedding is a mathematical representation of a chunk of text (or an image, or an entire site) translated into hundreds—or thousands—of numerical dimensions. Think of it as an address in “meaning space.” LLMs learn to place semantically similar pieces of content near one another; the closer two vectors are, the more alike their meaning.

How the Process Works

Tokenize: The model breaks sentences into tokens (words or sub-words).
Project: Each token is mapped to a high-dimensional coordinate based on training data.
Aggregate: Tokens combine (often via averaging or attention mechanisms) into a single vector for the entire passage.
Compare: When a user searches, their query is embedded the same way. A cosine-similarity calculation measures how close that query vector is to every document vector in the index.
Return: The engine ranks documents whose vectors sit nearest to the query—before any traditional ranking factors kick in.

Why Embeddings Trump Exact Keywords

Imagine two pages:

Page A: “A marathon is 26.2 miles long.”
Page B: “How far do runners travel in a marathon?”

Old-school keyword matchers might miss Page B for the query “marathon distance.” Vector embeddings recognize the semantic equivalence because both vectors converge in meaning space.

EEAT in a Vector World

Google’s quality framework—Experience, Expertise, Authoritativeness, Trustworthiness (EEAT)—is increasingly modeled with embeddings. Authors, pages, and entire domains are vectorized; Google can then calculate how consistently an entity writes about a given topic. Publish 60 in-depth articles on periodontics, and your author vector crowds into the “dental expertise” cluster—boosting perceived authority without a single link-building outreach email.

Conversely, scatter content across unrelated niches (sneakers one day, marine biology the next) and your site vector diffuses—diluting topical focus and relevance.

Practical Steps to Engineer Relevance

1. Chunk Content into “Fraggles”

AI Overviews rarely quote whole articles; they lift fraggles—tiny, self-contained passages that answer a micro-question. Keep sections concise (roughly 50-150 words) and laser-focused on a single idea. Use descriptive H2/H3 headings so retrieval systems pinpoint the right paragraph instantly.

2. Embrace Semantic Triples

Write sentences that explicitly frame relationships: Subject → Predicate → Object.

“Vector embeddings map words to high-dimensional space.”
The clearer the predicate, the easier it is for retrieval algorithms to detect your answer.

3. Expand Vocabulary with Contextual Entities

Include synonyms and closely related entities—LLM, cosine similarity, semantic hashing—to beef up contextual signals. This isn’t keyword stuffing; it’s adding semantic scaffolding that clarifies the topic’s perimeter.

4. Use Structured Data Everywhere

Schema markup remains the fastest way to hand AI “feature-rich” metadata. As knowledge graphs merge with LLMs, JSON-LD becomes a lighthouse in the semantic fog, guiding both ranking and answer synthesis.

5. Audit with Embedding-Based Tools

Modern SEO suites now offer relevance scores based on cosine similarity to a topic vector. Treat anything below your chosen threshold as a candidate for revision or pruning. That’s Relevance Engineering in action—quantifying what used to be a gut check.

Common Myths Busted

Myth	Reality
“Just add more keywords; LLMs will figure it out.”	Keyword density is noise in a semantic model. Quality, structure, and topical focus win.
“AI Overviews kill organic traffic, so why bother?”	Early data shows click-through rates drop, but the traffic that does click is highly qualified. Don’t forfeit that edge.
“Author bios satisfy EEAT.”	Bios help disambiguate names, but true authority comes from a body of semantically consistent work.
“Vector SEO is only for big enterprise sites.”	Any CMS can output structured data, and free embedding APIs let even small blogs test cosine similarity.

The Road Ahead: Search Without Blue Links?

As AI Mode rolls out, entire industries are bracing for fewer clicks and more zero-click answers. Some publishers see this as existential; others see opportunity. Whichever camp you’re in, one fact is clear: semantic relevance is the new table stake. The brands that engineer content for machine comprehension—vector-friendly passages, structured context, demonstrable topical depth—will surface in chatbots, voice assistants, and whatever interface comes next.

Meanwhile, behavioral metrics still police quality. If users bounce from an AI answer back into the SERP—or worse, reformulate the query—that negative signal feeds the loop. Relevance Engineering thus spans both retrievability (be the right vector) and satisfaction (earn the engagement).

Key Takeaways

Vectors are the language of modern search. If your content isn’t embedding-friendly, it’s invisible to the first stage of ranking.
Deep pages matter. Google’s AI Overviews overwhelmingly cite internal resources, not homepages. Optimize accordingly.
EEAT is measured mathematically. Consistent topical publishing tightens your entity vector, signaling expertise without manual “author tag” hacks.
Structured data future-proofs visibility. As LLMs cross-pollinate with knowledge graphs, schema markup becomes non-negotiable.
Relevance Engineering > traditional SEO. Treat visibility as an engineering problem—quantify, iterate, and scale.

Ready to Engineer Your Future?

Semantic search isn’t coming; it’s here. If you’d rather lead than react, start embedding-minded content workflows now. Not sure where to begin? Book a strategy call with our team, and let’s turn your site into a machine-readable, AI-ready authority—before your competitors figure out why their keyword tweaks stopped working.