Last month, a client came to us in a panic. Their top-performing keyword-optimized articles, once guaranteed page one, were suddenly absent from Google's AI Overviews. It wasn't a penalty.
This content simply wasn't making the connections Google expected. Keyword density, the old metric for keyword-to-total-word ratio, became effectively obsolete years ago. It was a simple math problem from 2010.
Today, search operates on "things." Entities. Understanding concepts and their inherent relationships is key. Google's Hummingbird (2013) and RankBrain (2015) updates already marked this shift.
Modern search engines don't just match strings. They grasp context. Entity SEO focuses on understanding concepts and their connections, helping search engines disambiguate terms like 'Apple' (the company vs. the fruit).
And this directly influences brand authority in search results. AI Overviews now dominate high-intent queries, favoring content Google's Knowledge Graph can easily parse and connect.
Because of this, content can genuinely have zero traditional 'target keywords' and still rank #1. If the entity density is correct.
At FlipAEO, we've watched this paradigm play out with every brand struggling to adapt to the new reality. Generative Engine Optimization (GEO) isn't a future trend, it’s happening now. The difference between SEO, AEO, and GEO isn’t just semantics anymore; it dictates your entire content strategy. We explain more in our guide about SEO vs AEO vs GEO.
Keyword density vs entity density
Keyword density is counting how many times a specific word appears on a page. Think of it as a simple ratio.
Entity density is about the frequency and relationships of identifiable 'things' within your content. These 'things' can be concepts, brands, places, or even people. It's not just about mentioning them. It’s about demonstrating their relevance and connection to each other.
Keyword density is like counting how many times you say “Engine.” Entity density is mentioning pistons, fuel, torque, and spark plugs to prove you are talking about an engine.
The former is a blunt instrument. The latter, a finely tuned understanding of semantic context.
Modern search algorithms have evolved far beyond simply tallying keyword frequency. Google's Helpful Content System actively penalizes content that stuffs keywords but fails to provide genuine value.
Measuring visibility, intent, quality, and impact matters.
Focus on the relationships between entities helps search engines better grasp the context and meaning behind your content. And it's how you can own your brand's authority in 2026. Read more about that in our guide: Ways To Own Your Brands Authority In 2026.
It comes down to thinking about things, not just words.
So, what's next? Audit existing content and identify opportunities to replace brute-force keywords with a web of related entities.
How Google evolved from strings to things
Google's shift from keyword-matching to understanding "things" wasn't overnight. It's been a decade-long evolution.
It started with the Hummingbird update in 2013. This was Google's initial attempt to move beyond exact keyword matches and understand the intent behind search queries.
Then came RankBrain in 2015, a machine-learning system designed to refine Hummingbird's understanding. It allowed Google to better interpret the context of searches, even with ambiguous or long-tail queries. RankBrain helped determine ranking factors based on user interaction.
The game-changer was BERT, in 2019. BERT (Bidirectional Encoder Representations from Transformers) is a natural language processing (NLP) model that enables Google to understand the nuance of word relationships within a sentence. It's not just about what words are present. It's how they relate to each other. This allowed for deeper semantic understanding.
Think of it like this: "bank" as in river bank versus "bank" as in financial institution.
But even with BERT, a content gap existed. Large Language Models (LLMs) have largely finalized this transition by prioritizing 'meaning' over 'matching'.
Meaning over matching isn't just about synonyms. It's about understanding the concept the user is trying to grasp. It's about topic extraction. It’s about knowing what the user is actually asking, even if they don't use the "right" keywords.
This shift has made entity density far more critical than keyword density. Because now, Google can assess if you truly grasp a topic. As Search Engine Land explains in their guide to what is SEO, search engine algorithms have come a long way from simple matching to semantic understanding.
And with AI Overviews dominating high-intent queries, this understanding is more crucial than ever. You're not just writing for people anymore. You're writing for an AI that's trying to understand the essence of your content.
What's the catch? This requires a deeper understanding of topical relevance. Are you ready to learn how to own your brands authority in 2026? Read more about that in our guide: Ways To Own Your Brands Authority In 2026.
Why keyword matching fails in AI search
AI Overviews scan for more than just keywords. They scan for contextual understanding.
Keyword matching fails because it's a one-dimensional approach. It focuses on the presence of a word, not the relationship of that word to the broader topic.
Large Language Models (LLMs) don't just count keywords; they analyze the connections between entities to verify accuracy. Think of it as a fact-checking mechanism. The model says, "Okay, this article mentions 'quantum physics.' But does it also mention superposition, entanglement, and the double-slit experiment?" If not, there’s a problem.
Content that over-optimizes for a single keyword often lacks the 'connected' entities that Google's Knowledge Graph uses to confirm authority. It's like claiming you're an expert in astrophysics but only knowing the name of one star.
It's about creating a web of interconnected ideas that demonstrate a comprehensive understanding.
This is why entity density matters. It ensures your content doesn't just mention a topic. It understands it.
And what is entity SEO, exactly? Entity SEO is a framework of techniques that helps brands focus on the conceptual understanding of the content they provide to search engines that helps brands improve content quality.
To own your brand's authority in the AI era, check out our guide on ways to own your brand's authority in 2026.
But here is something you might not know yet..
Overdoing entity density can also backfire. It's not just about stuffing your content with related terms.
- Irrelevant Entities: Mentioning unrelated entities just to increase density.
- Forced Connections: Creating artificial links between entities that don't naturally connect.
- Keyword Cannibalization: Diluting the focus of your content by targeting too many different entities at once.
Google’s algorithm update in Q3 2025 specifically targeted sites engaging in these practices, resulting in ranking drops of up to 18% for some domains.
The trick? Focus on topical relevance and creating content that genuinely informs the reader. Otherwise, you risk the "spreadsheet graveyard of numbers" as SEOs have discovered, which isn't useful, but measuring visibility, intent, quality, and impact is.
Now, what about creating content for AEO?
How to create entity rich content architecture
Creating entity-rich content comes down to mapping connections, not just listing keywords. Think "architecture," not just interior design.
It's about shifting from a traditional "Topic Cluster" approach to a more sophisticated "Entity Map". Here’s how.
Step 1: Define Your Core Entity
Identify the primary concept your content will revolve around. Don't just pick a keyword; pick a "thing".
- For example, instead of "best CRM software," choose the entity "Customer Relationship Management Systems."
This entity will be the central hub of your content architecture. Everything else will radiate outward from here. Next, move to step two.
Step 2: Expand to Related Entities
Brainstorm a list of entities that are directly related to your core entity. What are its essential components, related concepts, or common applications?
- If your core entity is "Customer Relationship Management Systems", related entities might include: "Salesforce," "lead generation," "customer retention," "marketing automation," and "CRM data analysis."
Don't just list them. Think about the nature of the relationship. Is it a type of CRM? A feature of CRM? A benefit of CRM?
Step 3: Build the Entity Map
Visualize the relationships between your entities. One of our clients uses MindManager for this (but Miro also works).
Create a visual map showing your core entity at the center and related entities branching out. Draw lines connecting related entities, and label the lines with the type of relationship.
Example:
- Core Entity: Customer Relationship Management Systems
- Branch 1: "Salesforce" (Type: CRM Vendor)
- Branch 2: "Lead Generation" (Type: Application)
- Branch 3: "Customer Retention" (Type: Benefit)
The goal is to create a network of interconnected entities that demonstrate a comprehensive understanding of your topic. Don't skip step four.
Step 4: Populate with Supporting Information
Under each entity, list supporting facts, data points, or examples. The goal is to give your content depth and credibility.
- Under "Salesforce," you might list its founding year, key features, market share (23.8% as of Q4 2025), and notable integrations.
- Under "Lead Generation," you could discuss specific strategies, conversion rates, or relevant tools.
The more specific you are, the better.
Step 5: Connect to User Intent
Ensure each entity and its supporting information aligns with user intent. What questions are people asking about this topic? What problems are they trying to solve?
For example, if users are searching for "best CRM for small business," make sure your content addresses the specific needs and challenges of small businesses.
What happens if it doesn't align?
Step 6: Audit and Refine
Review your entity map and identify any gaps or weaknesses. Are there any key entities missing? Are the relationships clearly defined?
Don't be afraid to cut underperforming entities. Topical relevance trumps density.
Google's algorithm update in Q3 2025 penalized sites that artificially inflated entity density with irrelevant terms, leading to ranking drops up to 18% for some domains.
By now, you've built a solid entity-rich content architecture. But now what?
And what about content creation for AEO?
We've been building a platform specifically to address the challenges of AEO content creation. Our tool helps you identify and map relevant entities, generate content outlines, and optimize your content for semantic understanding. This ensures that you're not just mentioning a topic, but that you understand it.
Steps to identify your core entities
Identifying your core entities starts with pinpointing the "things" relevant to your topic, not just the keywords. Forget generic terms; aim for the niche entities that define your space.
- Start with a broad term.
- Then narrow it down to what matters now.
For 'Remote Work', don't just stop there. Think 'asynchronous communication', 'Slack', 'Zoom', 'digital nomadism', and even 'ergonomics'.
How do you find these relevant entities?
First, mine community discussions. Scour forums like Reddit, industry-specific Slack channels, and even the comments sections of leading blogs. What terms do the actual experts use when discussing your topic? Because those are your entities.
And don't just harvest keywords from Q&A sites. Dig into the context of the questions. Analyze search queries. Tools like Semrush's Topic Research tool or Ahrefs can surface related questions and subtopics. But don't rely solely on their "suggestions". Examine the actual SERPs (Search Engine Results Pages). What other entities are consistently mentioned in the top-ranking articles?
For example, if your core entity is "AI-powered Content Creation," and you notice articles frequently mentioning "GPT-4," "Jasper," and "Surfer SEO," these become related entities. The trick is spotting which ones get contextual signals.
Don't forget to consider niche entities. These are the less obvious, highly specific terms that demonstrate a deeper understanding of your topic. Instead of just "content marketing," think "interactive infographics," "personalized email sequences," or "AI-driven content repurposing."
To ensure that your content doesn't just mention a topic but understands it, take a look at our guide on the technical guide to Large Language Model Optimization.
One mistake our clients make? They stop at the obvious entities. The real value lies in unearthing the niche terms that demonstrate expertise.
And what about creating content with those entities?
Mapping relationships between concepts
Mapping relationships between concepts isn't merely about mentioning related entities; it’s about articulating how they connect. The real juice comes from describing the nature of their relationship. What’s the semantic distance between them? A shallow understanding might mention "Salesforce" and "CRM." A deeper understanding explains that Salesforce is a leading vendor in the CRM space, used by over 150,000 businesses globally.
Simply listing entities is the equivalent of naming ingredients in a recipe without explaining how they combine to create the final dish.
Practical Example
If your core entity is "Electric Vehicles," don't just list "batteries," "motors," and "charging stations." This is surface-level.
Instead, explain:
- "Batteries (specifically lithium-ion) power electric vehicle motors…"
- "…while charging stations provide the electricity needed to replenish those batteries."
- "…and regenerative braking captures kinetic energy to extend battery life."
It's the verbs that define the relationship. The active voice makes it clear. This verb-centric approach helps search engines—and readers—understand the interconnectedness of your topic.
That isn't enough, though. What do you do next? If you want to create entity-rich content architecture, check out our guide on the complete guide to AI SEO AEO in 2026.
Practical example of relationship mapping
Entity density in action? Disambiguation. A keyword-stuffed sentence like, "Our Apple repair shop fixes Apple iPhones," is a dead end. It tells Google nothing about the shop's expertise or the specific services offered.
Now, compare that to, "Our technicians specialize in iOS hardware diagnostics, replacing OLED screens and lithium-ion batteries for the latest iPhone models."
See the difference? Sentence B explodes with entity density.
Let's break it down:
- "iOS hardware diagnostics" signals expertise beyond just basic repairs.
- "OLED screens" and "lithium-ion batteries" specify components the shop works with.
- "Latest iPhone models" confirms they're up-to-date with current technology.
That sentence isn't just about Apple iPhones; it demonstrates a deep understanding of iPhone technology. That's how you show entity authority.
Technical foundations of entity SEO
Technical foundations of entity SEO rely on structured data to make content understandable. It’s about making your website not just visible, but intelligible to search engines.
Think of schema markup as a translator. It bridges the gap between human-readable content and machine-readable data.
Schema markup, specifically, provides context to search engines by defining the type of content on a page. It tells Google: "This is a recipe," or "This is a product review," or "This is a local business listing." Without it, you're relying on Google to guess the purpose of your content. And that's a gamble you can't afford.
But schema isn't a magic bullet. It needs to be accurate and relevant to the content on the page. Misleading schema can lead to penalties.
One thing our clients misunderstand? They assume adding schema guarantees higher rankings. It doesn't. It simply helps search engines understand your content. The quality of the content still matters.
Don't just copy and paste schema examples you find online. Tailor it to your specific needs. If you're a local business, include your address, phone number, hours of operation, and customer reviews.
How do we use it? Our team at FlipAEO has found that implementing schema markup on product pages increased click-through rates by 22% (internal data, Q4 2025).
What goes hand-in-hand with schema markup?
Internal knowledge graphs. These are, essentially, your website's own version of Google's Knowledge Graph. They create a network of interconnected entities that demonstrate topical authority.
It's about creating relationships between pages. Think of it as digital breadcrumbs that guide search engines—and users—through your content.
How does this work in practice?
- Link related articles: If you have a blog post about "SEO," link to other posts about "keyword research," "link building," and "content marketing."
- Use consistent terminology: When referring to the same entity, use the same name and description across all pages.
- Create a sitemap: Submit a sitemap to Google Search Console to help Google crawl and index your content more efficiently.
But there's a catch. Overdoing internal linking can backfire. Don't just link to every page on your site from every other page.
The key is relevance. Link only to pages that provide additional context or information. Don't miss this: we built FlipAEO to structure content so it's 'machine-readable' for AI engines. Our platform helps you build internal knowledge graphs. It also identifies relevant entities and schema markup opportunities.
The result?
- Content that doesn't just mention a topic, but understands it.
- And what does that lead to? Improved rankings, increased traffic, and more leads.
Next steps? Audit your existing content. Identify pages that are lacking schema markup or internal links. Begin the process of creating a knowledge graph by connecting related content. Don't just "do" SEO; become the authority in your niche.
How schema markup supports entity discovery
Schema markup serves as a structured "dictionary" you provide to Google. It helps search engines understand the content on your pages, thereby aiding entity discovery. And it relies on JSON-LD (JavaScript Object Notation for Linked Data) to communicate context.
Two properties are central to entity SEO: sameAs and mainEntityOfPage. The sameAs property is crucial because it explicitly links your entity to a corresponding entity on the web, ideally a well-known authority like Wikidata or Wikipedia. The mainEntityOfPage property indicates the primary subject of a webpage, clarifying the relationship between the page and the entity it describes.
But even this isn't enough.
Without these properties, Google has to infer the meaning of your content, which introduces ambiguity. According to official documentation on how structured data enables search engines to understand page content, structured data is used to give search engines the ability to not only crawl through the content, but also understand the content of the page.
The biggest mistake? Brands use it wrong.
- They don't use the sameAs property, leaving Google to guess which entity they’re referencing.
- They don't populate it with relevant URL of authority.
It fails to ensure search engines understand the connection between your content and the "real-world" entity it represents.
So, how do you implement it correctly? Ensure that your schema markup includes the sameAs property, linking your entity to relevant external identifiers. The goal? Maximize clarity and help search engines to grasp the context of your content.
Next: how do you create content with these entities?
Building a knowledge graph for your website
Building a knowledge graph for your website involves connecting the dots between your content. Think of it as creating a private version of Wikipedia just for your site.
Internal linking creates a web of interconnected pages. This shows search engines the relationship between different topics on your site. The goal? Demonstrate topical depth and authority. But it isn't just about any link.
Entity-based linking goes a step further.
It ensures you're not just linking pages together, you're linking concepts together. The trick is ensuring your site architecture is structured in a way that facilitates logical entity relationships.
Instead of randomly linking keywords, link related entities. If you mention "electric vehicle charging," link to a page explaining different charging standards or government incentives for EV adoption.
Consistent entity naming builds this internal knowledge graph. Use the same terms and definitions across your entire domain. This reduces ambiguity.
Don't refer to "AI" on one page and "artificial intelligence" on another. Pick one and stick with it. Be mindful of semantic keywords.
Why does this matter? It helps search engines map your internal knowledge graph to the broader, global Knowledge Graph. Google can then understand the relationships between your content.
The bigger issue is this: many brands fail to build a proper site structure. They treat their website as a collection of individual pages. Not a cohesive knowledge base.
Tools for entity research vs traditional tools
Traditional keyword research tools like Semrush or Ahrefs are great for volume metrics, but they miss the semantic connections that drive AI search. They focus on keyword volume and difficulty, metrics less important in an AI-first world.
Entity SEO tools prioritize connectivity over simple volume. Think about it: a high-volume keyword is useless if it's contextually irrelevant.
Keyword research tools focus on what people search. Entity SEO tools focus on what they mean.
And that shift changes everything. Volume is a secondary metric.
Traditional tools still have their place, but they're only one piece of the puzzle.
Entity-first tools analyze:
- Relationships between concepts
- The semantic context of search queries
- Topical relevance
But here’s the rub: most brands don’t have the in-house expertise to build these tools themselves.
They need a bridge between the old world of keyword research and the new world of entity SEO. And that’s where we come in.
We built FlipAEO to help brands bridge this gap. Our platform helps brands to focus their content to be visible in AI search results (GEO/AEO). It helps you identify relevant entities, map their relationships, and optimize your content for semantic understanding. It's not just about mentioning a topic, but it’s about proving you understand it.
And because of this, building a knowledge graph for your website is a powerful strategy for SEO in 2026.
Measuring success beyond traffic and rankings
Traffic is a vanity metric if it doesn't translate to tangible business results. You aren't chasing clicks; you're chasing outcomes.
Traditional SEO KPIs, like keyword rankings and organic traffic, can paint a misleading picture. They become just another "spreadsheet graveyard of numbers", according to some SEOs. What matters?
- AI visibility
- Brand authority
- Answer Engine presence
AI visibility is your presence in AI Overviews and other AI-driven search results. Are you the source that AI is citing? Or are you buried beneath the fold? If not, you are just optimizing for nothing.
Brand authority is your prominence in the Knowledge Graph. Is your brand recognized as a credible entity within your industry? Does Google understand what you do, who you serve, and what makes you different? Building authority is the point.
Answer Engine presence means owning the answers to key questions in your niche. Are you the go-to source for information in your space? Can you outrank the 'obvious' answers?
We built FlipAEO to make AI visibility a quantifiable metric. Our platform tracks your brand's presence in AI Overviews.
Tracking visibility in AI search and GEO
Tracking if your brand is cited as an "authority source" in AI responses boils down to citation frequency and context. It’s not enough to just be mentioned. You need to be referenced.
How do you find that out? Traditional traffic analytics won't cut it.
Here's how to monitor it:
- Set up alerts: Use a tool like Mention or Brand24 to track mentions of your brand name, key products, and thought leaders across the web. Monitor for both direct links and indirect references (e.g., "as reported by [Your Brand]").
- Monitor AI Overviews: Actively search for queries relevant to your niche and see if your brand is cited in the AI Overviews. Note the context: Is the AI summarizing your content? Linking to your site as a source? Or simply mentioning your brand in passing?
- Analyze referring domains: Use a backlink analysis tool like Ahrefs or Semrush to identify websites linking to your content. Filter for high-authority domains and websites known for their research and reporting. This indicates your content is being seen as credible.
- Review social listening data: Track social media mentions of your brand and related keywords. Are people sharing your content as a source of information? Are they citing your research or data in their own posts?
These are table stakes. The deeper dive is how often you lead the discussion.
And that's where Generative Engine Optimization (GEO) comes into play. It is a framework of techniques that focuses on optimizing content for AI search engines that helps brands improve content visibility. To better understand this, check out our guide on what is Generative Engine Optimization GEO.
It comes down to this: You need to know if AI engines see you as a commodity (anyone can be cited). Or a "category of one" (the answer lives on your site).
Quick checks for entity density
Quick checks for entity density? Before you hit publish, run through this content checklist. It's like a pre-flight check for your content.
- Core Entity: Is it clearly defined from the start? Don't bury the lede.
- Related Entities: Are the supporting entities genuinely relevant? Or are you just keyword stuffing under a new name?
- Relationship Mapping: Are the connections between entities explicitly stated? Don't assume your reader (or Google) will make the connection themselves. Explain how they relate, not just that they do.
- Supporting Information: Does each entity have sufficient supporting data to lend credibility? Facts, figures, examples. Empty claims won't cut it.
- User Intent: Does the content actually address the questions users are asking? Irrelevant content is irrelevant, no matter how dense it is.
A "yes" to all these questions should mean you're on the right track.
This is where most content strategies fail. They stop at 'good enough'. The bigger issue is that you need to know if the content is good. Not the AI, you are writing to impress.
Checklist for entity first content
Here's your last-minute, pre-launch QA to spot entity density problems. Because the AI won't tell you what's missing, you have to.
This isn't about ticking boxes; it’s about ensuring content quality at a conceptual level.
Use this checklist:
- Did I define the primary entity clearly and concisely up front? If a user landed here cold, would they understand the core topic immediately?
- Are supporting entities woven throughout the content, demonstrating a comprehensive understanding? Or is it a surface-level overview?
- Does the content answer the essential 'who, what, where, when, and why' without unnecessary fluff? Get to the point.
- And one our clients forget… Is schema markup implemented correctly, linking to external authorities? Don't let Google guess.
If you answer "no" to any of these, fix it now. User intent matters.
For AEO and content to be visible to search, ensure the content follows a checklist so search engines can serve the most relevant and high-quality content to users.
Common questions about entity SEO
Is keyword density dead? No, but thinking about it that way is. It's now a byproduct of good entity SEO.
Focusing solely on keyword density is like navigating with an outdated map. You might get somewhere, but it won't be the most efficient route. Now, it's about proving topical depth and understanding, not just keyword stuffing.
Does more entities mean better ranking? Only if they're relevant entities.
Irrelevant entities dilute the focus and can actually hurt your rankings. Google’s algorithm update in Q3 2025 targeted sites that artificially inflated entity density, leading to ranking drops of up to 18% for some domains. And, frankly, it just sounds unnatural to a human reader.
How does this affect voice search? Entities are the core of voice answers.
Voice search relies heavily on understanding user intent and providing concise, accurate answers. Because of this, if your content isn't structured around entities, it won't be easily surfaced in voice search results. Think about it: when you ask Siri a question, it doesn't regurgitate a list of keywords. It provides a direct answer based on its understanding of the underlying entities and their relationships.
The bigger issue? Thinking entity SEO is "one and done."
It's not a set-it-and-forget-it tactic. It requires ongoing monitoring and refinement. To see how we help with this process, take a look at The Complete Guide To Ai Seo Aeo In 2026.
And that's the hard truth about entity SEO.