Entity SEO: How to Get Google and AI Assistants to Describe Your Brand Correctly

Entity SEO is the work of making search engines and AI assistants recognize your company, your product and your category as distinct things with clear facts attached. Google stores those facts in its Knowledge Graph. ChatGPT, Perplexity, Gemini and Copilot build their own picture from the pages they read and the sources they retrieve. When both pictures agree with what you say about yourself, you show up correctly in knowledge panels, AI Overviews, AI Mode and assistant answers. When they disagree, an assistant tells a buyer you lack a feature you shipped last year.

That second scenario is why entity work moved up my list in 2026. At Flying Cat I lead strategy on every account. The first AI audit for a new B2B SaaS client usually turns up at least one wrong description. A pricing model from two years ago, an old product name or a category label the company has dropped all count. This guide covers how entities work, how to make your entity data consistent across your site and the web, which schema to add, how to correct a wrong AI description and how to measure whether the machines understand you.

What an entity is in SEO

An entity is a uniquely identifiable thing, such as a company, a person, a product, a place or a concept. Google introduced its Knowledge Graph in 2012 under the phrase "things, not strings". The idea was to store facts about things and the relationships between them, so a search for "Apple" could resolve to the company or the fruit based on context. By 2020 Google said the graph held over 500 billion facts about five billion entities, drawn from public web content, licensed databases and the structured data site owners provide.

A keyword is the string a searcher types. An entity is the thing that string points to. "Brightspace", "D2L Brightspace" and "the D2L LMS" are three strings for one product entity. Entity SEO makes sure every system that reads about you connects all of those strings to the same thing, with the same facts.

For a B2B SaaS company, the entities that count are usually these.

• Your organization: legal name, brand name, founding date, headquarters, founders and leadership, parent company.
• Your products: product names, the category each belongs to, pricing model, integrations, the customers and industries it serves.
• Your people: the founders and subject experts whose names appear on your content and in the press.
• Your category and its concepts: the problems you solve and the terms buyers use for them, such as "procure-to-pay" or "AI veterinary scribe".

The language side of this, how search engines parse sentences and extract meaning from content, sits in my guide to NLP in SEO. This page stays on the entities themselves and the data that describes them.

Why entities are the bridge between Google and AI answers

Google's Knowledge Graph and a large language model are two different systems. The Knowledge Graph is a structured database of facts. An LLM carries a compressed impression of everything it read in training, then adds whatever it retrieves from the web at answer time. Both systems form their picture of your brand from the same raw material, which is your website and what the rest of the web says about you.

That shared input is what makes entity SEO useful for AI visibility. When your site, your schema, your LinkedIn page, your G2 profile and the roundups that mention you all describe your company the same way, Google has a confident entity to show. An assistant retrieving five sources to answer "what does [your product] do" also gets five matching answers. When those sources conflict, both systems have to guess.

Several pieces of public evidence point the same way.

• Google's documentation on AI features and your website says AI Overviews and AI Mode have no special requirements beyond being indexed and eligible for a snippet. It also says no special schema is needed. Among its best practices, it lists matching structured data to visible text. The same page explains that both features use query fan-out, running several related searches across subtopics before writing an answer.
• Microsoft's Fabrice Canel said at SMX Munich in March 2025 that Bing uses schema markup to help its LLMs understand content, which feeds Copilot.
• Ahrefs studied 75,000 brands and found that branded web mentions correlated with AI Overview brand visibility at 0.664, far ahead of backlinks at 0.218. Mentions describe your entity across the web, so this result says the off-site description of your brand carries more weight than raw link counts.

My own research adds a caution. In my Search Spot study of 488 product pages from Forbes Cloud 100 companies, none of the 14 on-page traits I measured correlated with AI citations above 0.15 once rankings were held equal. Product schema and FAQ schema were on that list. Descriptive copy that said plainly what the product does correlated with citations at 0.19, while benefit-led copy correlated at -0.18. Schema helps machines read your facts. Clear, specific facts on the page are what earn the citation.

Step 1: Write an entity fact sheet

Every entity project I run starts with one document that states the facts about the company the way the company wants them stated. We call it the entity fact sheet. It becomes the reference for the website, the schema, every third-party profile and every correction request.

Here is the template we use, filled in for a fictional company.

Two fields carry the heaviest load. The one-sentence description should be literal and specific, because assistants repeat literal sentences. The category label should be the term buyers use, which you can confirm in Google Search Console queries and in the prompts buyers type into assistants. My guide to AEO prompt research covers how we find those prompts.

Product marketing should approve the sheet. At D2L, the product marketing team supplied approved positioning. We then reworked every competitive page to show Brightspace at its best. That alignment is part of why D2L went from appearing in 12% to 33% of tracked AI answers between December 2025 and September 2026.

Step 2: Make your own site consistent

Your website is the source you control completely, so fix it first. Assistants retrieve your pages directly. Google uses them to confirm what it already believes about you.

Run through these checks against the fact sheet.

• One name everywhere. Search your site for every retired product name and nickname. Old blog posts are the usual culprits. Update them or redirect them.
• A literal About page. State the one-sentence description, founding facts, leadership and headquarters in plain text. Avoid burying them in images or animation.
• Product pages that describe the product. Lead with what it does, who it serves, the integrations and the pricing model. My Search Spot data showed pricing pages had a median of 52 AI citations, against 21 for platform pages and 1 for solution and integration pages. Assistants answer pricing questions from your own site, so an outdated pricing page spreads outdated facts.
• Author pages for your experts. Give each named author a page with their role and credentials. Link their bylines to it.
• Internal links that reflect relationships. Link each feature page to its parent product page. Link category explainers to the product that solves the problem. Use the entity name in the anchor text. This builds a small version of a knowledge graph inside your site.
• Pages that machines can read. If your site renders content with JavaScript, check what Googlebot and AI crawlers actually receive. My guide on whether AI can read your site walks through the tests.

Bizzabo is a good example of this kind of cleanup. We regrouped its content library into clusters linked to product and solutions pages, pruned 63 URLs that wasted crawl budget, fixed redirect chains and orphan pages, and added FAQ, How-To and Product schema. Organic visits to product and solutions pages rose 67% in four months.

Step 3: Add Organization and Product schema and connect them

Structured data states your entity facts in a format machines parse without guessing. The goal for entity SEO is a connected set of schema, where every page refers to the same organization and product by a stable identifier.

Organization schema

Google's Organization structured data documentation recommends placing this markup on your home page or on one page that describes the organization, such as the About page. It has no required properties. Google recommends adding every property that applies, including name, legalName, description, url, logo, sameAs, foundingDate and contact details. The logo property can help Google choose the logo shown in search results and the knowledge panel.

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The @id is the piece people skip. It gives your organization a permanent identifier that other markup on the site can point to. The sameAs list tells machines which external profiles describe the same entity, so include only official profiles you control or that clearly describe you.

Product schema

For the product, Google documents SoftwareApplication as the type for app details. Its rich result needs a name, a price in offers and a rating or review. Many SaaS sites use Product markup instead. Either way, the entity job is the same. Name the product exactly as the fact sheet does, describe it literally and connect it to the organization by @id.

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Add rating markup only when the reviews appear on the page and follow Google's review guidelines. Keep every value in the markup identical to the visible text, since Google asks for that match. Then validate the result in Google's Rich Results Test and the Schema Markup Validator.

Person schema for authors

Mark up each author page with Person schema, including jobTitle, worksFor pointing to the organization's @id, and sameAs links to the person's LinkedIn profile. This ties your experts to your company entity, which supports the experience and expertise signals in Google's guidance on helpful content.

Step 4: Align third-party sources

Your site is one source. Google and the assistants read many more. The third-party sources that shape your entity fall into four groups.

Review sites deserve extra attention. They rank for category queries, assistants retrieve them often and their product descriptions are frequently years old. Update your vendor profile on each one with the fact sheet's description, category and pricing model.

Roundups are the second priority. My Search Spot analysis of 93,968 AI answers found that listicles earned 60.9% of AI citations in Q3 2026, with review sites and directories rising from 5.8% to 7.4%. When a roundup describes you incorrectly, that description travels into every answer that cites it. The enterprise procurement platform we work with earned placements on the third-party roundups and industry publications AI assistants use as sources. It went from being named in 76% to 88% of ChatGPT category answers in four months. ChatGPT answers citing its own site also rose from 25% to 41%. My guide to off-site visibility covers how we choose which sources to pursue.

A Wikipedia article helps an entity. Many B2B SaaS companies do not meet Wikipedia's notability rules, though, and editors remove promotional articles. A Wikidata item with accurate, sourced statements is a more realistic step for many companies. Consistent profiles, press coverage and review site listings do the rest.

Step 5: Fix wrong AI descriptions of your brand

When an assistant gets your brand wrong, the error almost always traces back to a source. The assistant either learned an old fact in training or retrieved a page that states it. Correcting it is a long-term project. The order of work makes a difference.

1. Document the error. Run the prompt that produced it in each assistant and save the answer. Note which sources the assistant cites, when it shows them.
2. Fix your own site first. Search your site for the wrong fact. Old blog posts, outdated pricing pages, legacy press releases and old PDFs often carry it. Update or redirect every instance, then make sure the correct fact is stated plainly on the relevant page.
3. Fix third-party sources next. Update your profiles on review sites and directories. Ask the publishers of roundups and comparison pages that state the error to correct it, with the fact sheet as your reference.
4. Publish the correct fact where assistants look. If no page on the web states the right answer clearly, write one. A page that answers "Does Larkspur integrate with Salesforce" in its first sentence gives retrieval systems something to quote.
5. Recheck monthly. Retrieval-based answers can change within weeks of a fix. Answers that rely on training data change only when the model is retrained, which takes longer.

Google offers its own route for the Knowledge Graph. Many knowledge panels can be claimed by their subject through a verification process, after which the company can suggest changes.

Here is a worked example of the audit log we keep for each client, using the fictional company.

Step 6: Measure entity understanding through AI answers

The old way to measure entity SEO was to run pages through Google's Cloud Natural Language API and watch salience scores. The v1 API still returns salience. The v2 API, released in 2023, dropped salience and Wikipedia metadata in favor of a probability score. The tool also measures how one model reads one page, which tells you little about how Google or ChatGPT describes your company.

I measure entity understanding through the answers buyers actually see. At Flying Cat we track AI visibility in Scrunch with four signals.

• AI visibility: how often the brand appears across an agreed set of buyer prompts. Each prompt runs many times a month across several assistants, because single answers vary.
• Citations: how often the assistants use the client's own pages as sources.
• Direct and branded traffic growth: a sign that more people know the brand by name.
• AI bot traffic: crawls from AI systems for retrieval and training, which shows whether they reach your pages at all.

On top of that, an entity check adds accuracy. Each month, run a fixed set of brand prompts and compare every answer with the fact sheet. Good prompts include "What is [brand]", "What does [product] cost", "Who are [brand]'s competitors", "Does [product] integrate with [tool]" and "Who founded [brand]". Score each answer as accurate, partly accurate or wrong. Then watch which category label the assistants use for you, since that label decides which category prompts you appear in.

Two cautions keep the numbers honest. AI ranking position is unreliable from one run to the next, so report visibility percentage across many prompts instead of rank. Results also take time. We usually see movement within the first 60 days. Corrections that depend on model retraining take longer. The Leapsome case study shows the kind of curve to expect, with its mentions in Google AI Overviews growing 5x from March to August 2025. My AEO measurement guide covers the full reporting setup.

Common entity SEO mistakes

These are the errors I see when I audit a new client's entity setup.

• Treating schema as the whole job. Markup that describes facts the page never states does little. Google asks for structured data that matches the visible content. My research also found no on-page trait, schema included, that predicted citations once rankings were equal.
• Stuffing pages with entity names. Listing famous brands, tools and people to look topically complete adds noise. Mention the entities your buyers need for the answer. Then explain how they relate.
• Letting product names drift. A rename that reaches the homepage but not the blog, the review sites or the integration marketplaces leaves assistants with two products where one exists.
• Ignoring the off-site picture. A clean website cannot outweigh five roundups that call you by your old category. Mentions across the web carry a lot of weight, as the Ahrefs data shows.
• Measuring with tools that do not reflect answers. A salience score on one page tells you less than a monthly check of what ChatGPT says about your pricing.

Entity SEO also sits on top of the fundamentals. My Search Spot product page study found that 76% of product pages with five or more top-10 keywords reached 10 or more AI citations, against 10% below that threshold. Pages still need to rank, load and get crawled, which is why technical SEO comes before any entity cleanup when a site has crawl or rendering problems.

How Flying Cat runs entity SEO for B2B SaaS clients

Entity work is part of every GEO and AEO program we run. It follows the order in this guide. In the first weeks we write the fact sheet with the client's product marketing team, run the brand prompts across the assistants and log every wrong or outdated statement with its source. The technical audit covers schema, rendering and crawl access. Then we fix the client's own site, connect the Organization, product and Person markup, and update review site and directory profiles.

Off-site work follows. We pick the roundups and publications that assistants cite for the client's category prompts and pursue placements and corrections there. Every month we rerun the prompt set, track visibility and citations, and check accuracy against the fact sheet. For clients selling in Europe, we run the same checks in French, German, Italian and Spanish, since assistants often describe a brand differently by language.

If you are comparing entity SEO agencies, ask each one to show you the wrong statements assistants make about your brand today and where each statement comes from. An agency that can answer that in the first call understands entities. Every Flying Cat program starts with a $5,000 Strategy Launch over three to five weeks, which includes this audit. Book a call and I will show you how Google and the assistants describe your company right now.

Frequently asked questions about entity SEO

What is entity SEO in simple terms

Entity SEO is making sure search engines and AI assistants know exactly what your company and products are, with correct facts attached. You do it by stating those facts consistently on your site, in schema markup and across third-party sources such as review sites, directories and press coverage.

How is entity SEO different from keyword SEO

Keyword SEO targets the words people type. Entity SEO targets the things those words refer to and the facts about them. The two work together. Keywords help a page rank for a query. Entity signals help Google and AI assistants connect that page to the right company, product and category.

Does schema markup improve AI visibility

Schema helps machines read your facts without guessing. Microsoft has said Bing uses it to help its LLMs understand content. Google says no special schema is needed for AI Overviews or AI Mode. In my research, schema alone did not predict AI citations once rankings were equal, so treat it as hygiene alongside clear on-page facts and accurate third-party sources.

How long does it take to fix a wrong AI description of my brand

Answers that rely on retrieved web pages can change within weeks of fixing the source. Answers drawn from a model's training data change only after the model is retrained. We usually see AI visibility start to move within the first 60 days of a program, with full corrections taking several months.