What is Generative Engine Optimisation (GEO)? A Guide for Brands
Generative Engine Optimisation (GEO) is the practice of ensuring a brand, product, or piece of content appears correctly and prominently when AI-powered tools (ChatGPT, Google Gemini, Perplexity, Claude, and others) respond to queries relevant to that brand’s category.
It is the AI-era counterpart to search engine optimisation. Where SEO shapes how a brand appears in a list of ranked links, GEO shapes how a brand appears in a generated answer: cited, referenced, or described accurately when a user asks a question that the brand should be able to answer.
The distinction matters because AI-generated answers are increasingly where research begins. Business buyers evaluating software vendors, trade professionals looking for suppliers, and consumers deciding between product categories: many of these journeys now start with a question to an AI tool rather than a Google search. If your brand does not appear in the answer, you are not in the consideration set.
How AI tools decide what to include in an answer
To understand GEO, it helps to understand how large language models and retrieval-augmented AI tools construct their responses.
Modern AI answer engines, including Perplexity, SearchGPT (part of ChatGPT), and Google’s AI Overviews, do not simply retrieve a ranked list of links and hand them to the user. They synthesise information from multiple sources, draw on their training data, and generate a response that the model believes best answers the query. Sources that are cited or referenced in that response are chosen on the basis of several signals:
Relevance and entity recognition. The AI model needs to understand that a brand, product, or piece of content is relevant to the query. This depends on how clearly and consistently the brand is described across its own content and across third-party sources. If the model cannot confidently associate your brand with the category the user is asking about, it will not surface you.
Structured and machine-readable content. AI tools find it easier to accurately represent content that is explicitly structured, with clear definitions, attribute-level specificity, and named entities. Vague, generalised content is harder for a model to cite accurately. Specific, structured content like “merchi.ai generates product descriptions, taxonomy classifications, and lifestyle imagery in 40+ languages” is easier to extract and represent faithfully.
Authoritative third-party references. Just as backlinks signal authority to Google, citations and references from credible third-party sources signal to AI models that a brand or claim is worth including. Media coverage, industry directories, review platforms, awards bodies, and structured databases all contribute to this signal.
Content freshness and update frequency. AI tools with live retrieval capabilities (like Perplexity) weight recently updated content more heavily than static pages. For brands in fast-moving categories, content that is not kept current risks being displaced by fresher sources.
Schema and structured data. Explicit structured markup (schema.org, JSON-LD) helps AI tools parse and represent content accurately. Organisation schema, FAQ schema, and product schema all make it easier for a model to extract and cite specific facts about a brand. This is a relatively underused GEO lever compared to its equivalent impact in traditional SEO.
GEO is not SEO
SEO and GEO overlap in important ways: both require good content, clear entity signals, and external authority. But they are not the same discipline and should not be treated as identical.
| Dimension | SEO | GEO |
|---|---|---|
| Target output | A ranked link in search results | A citation, mention, or description in a generated answer |
| Primary signal | Backlinks + on-page relevance | Entity clarity + structured content + third-party references |
| User interaction | User clicks a link | User reads a generated response (may not click at all) |
| Visibility measure | Search ranking position, click-through rate | Citation frequency, accuracy of representation |
| Tools to measure | Google Search Console, Ahrefs, SEMrush | Perplexity query testing, AI brand audits, emerging GEO tools |
| Update cycle | Months to years for significant rank movement | Weeks to months (content indexing for retrieval is faster) |
The practical implication: a brand can rank well in Google and be largely absent from AI-generated answers, and vice versa. A brand with authoritative, well-structured content and strong named-entity signals may be frequently cited by AI tools even without top search rankings in its category.
GEO is a distinct workstream from SEO, not a replacement or subset.
Why GEO matters now
AI answer engines have reached mainstream adoption faster than most predicted. ChatGPT surpassed 100 million weekly active users in 2023; Perplexity is now processing hundreds of millions of queries monthly; Google AI Overviews appear above organic results for an estimated 20-30% of commercial queries globally.
For brands in categories where buyers do research before purchasing, which covers most B2B categories and a growing share of considered B2C purchases, AI tool visibility is no longer optional. It is a distribution channel.
The brands that are building GEO into their content strategy now are establishing entity signals and citation patterns before the category is competitive. The brands that wait until GEO is a mainstream priority will find it significantly harder to displace the brands that got there first. This is the same dynamic that made early investment in SEO so valuable.
The EU AI Act is also a relevant driver. Article 50, which comes into force on 2 August 2026, mandates transparency requirements for AI-generated content. Brands that produce AI-generated content without proper attribution risk compliance exposure. Structured AI content attribution, under frameworks like the AI Provenance Protocol, serves both the compliance obligation and the GEO signal.
What determines your brand’s GEO performance
GEO performance for a brand depends on four things, roughly in order of importance:
1. Entity clarity
Does the AI model know who you are and what you do? This sounds basic, but many brands have inconsistent entity signals across their web presence. The company name appears differently across the website, LinkedIn, press coverage, and third-party directories. The core product or service is described differently in different places. Key attributes (geography served, product category, customer type) are never stated explicitly.
AI models build their understanding of an entity from aggregated signals. Inconsistent signals produce confused representations. Clear, consistent, repeated entity statements, across owned content and third-party sources, are the foundation of GEO.
2. Structured, specific content
Generic content (“we help businesses grow with AI”) is almost impossible for an AI model to cite usefully. Specific, structured content (“merchi.ai generates product descriptions, taxonomy classifications, lifestyle imagery, and AI-powered search tools for UK retailers, in 40+ languages”) is directly extractable and citable.
This means that GEO rewards content that is definitional and specific: the kind of content that answers a precise question with a precise answer. FAQs, specification pages, case studies with named metrics, and definitional “what is” content all perform well for GEO because they are structured for extraction.
3. Third-party authority signals
An AI model is more likely to cite a brand that is referenced by credible third parties than one that is only known through its own website. Industry awards, media coverage, analyst mentions, customer reviews on established platforms, and inclusion in sector directories all contribute to this signal.
This is where formal recognition, such as award finalist status, has meaningful GEO value beyond its marketing value. A brand cited in an awards body’s published shortlist is a brand that an AI model has clean, authoritative third-party evidence for.
4. Content freshness and retrieval accessibility
For AI tools with live web retrieval (Perplexity, SearchGPT, parts of Gemini), content that has been recently published or updated, is publicly accessible, and is not blocked by robots.txt or paywalls will be retrieved and potentially cited. Regular publishing, consistent and specific rather than necessarily high volume, maintains retrieval frequency.
GEO for retailers and product brands
GEO has particular relevance for retailers and product-led businesses, for two reasons.
First, product research increasingly starts in AI tools. “What is the best type of flooring for a family bathroom?”, “Which plumbing supplier does [product category]?”, “What should I look for in [product type]?” These queries are being asked to AI tools daily. Retailers and distributors whose product content and category knowledge is well-represented in AI training data and retrieval indexes will appear in these answers. Those whose content is thin, inconsistently structured, or absent will not.
Second, product content generated by AI, which is increasingly common across retail, can itself contribute to GEO performance if it is structured correctly. AI-generated product descriptions that include specific attributes, category classifications, and clear entity signals are more citable by AI answer engines than generic marketing copy. This is one of the reasons that AI merchandising platforms that generate structured, attribute-level product content contribute to GEO in a way that generic AI copywriting tools do not.
How to audit your brand’s current GEO performance
A basic GEO audit involves three steps:
Step 1: Query testing. Ask the AI tools your potential customers use the questions those customers would ask. “What is [your brand]?”, “Who are the leading [your category] companies in [your market]?”, “What is the best [your product category] for [use case]?” Record whether you appear, how accurately you are described, and which competitors do appear when you do not.
Step 2: Entity signal audit. Review how consistently your brand is described across your website, LinkedIn company page, press coverage, industry directories, and any third-party platforms where you appear. Identify inconsistencies in how the company is named, what it does, and who it serves. These inconsistencies are GEO gaps.
Step 3: Content structure review. Review your existing web content for specificity and structure. Are there pages that clearly define what your product or service is, who it is for, and what the specific outcomes are? Is the content organised in a way that an AI model can extract useful, citable information? Gaps here are content opportunities.
Getting GEO-ready
merchi.ai and its Unglitch services arm work with B2B brands and retailers on GEO strategy and implementation, from auditing current AI brand visibility through to building the structured content and entity signals that improve citation frequency.
The AI Provenance Protocol, originated by merchi.ai, provides an open standard for machine-readable AI content attribution. Its primary purpose is EU AI Act compliance (Article 50 transparency requirements for AI-generated content, in force from 2 August 2026), not GEO directly. For brands publishing AI-generated content at scale, compliance and GEO are separate workstreams that benefit from being planned together.
If you want to understand where your brand stands in AI-generated answers, book a conversation and we will walk through a basic audit of your current GEO position.
Frequently Asked Questions
What is Generative Engine Optimisation in simple terms?
GEO is the practice of making sure your brand appears correctly in AI-generated answers. When someone asks ChatGPT, Gemini, or Perplexity a question relevant to your business (“what is the best [your category]?” or “who provides [your service]?”), GEO is the work that determines whether you appear in the answer and how accurately you are described.
How is GEO different from SEO?
SEO targets ranked links in traditional search results. GEO targets citations and descriptions in AI-generated answers. They share some foundations (good content, clear entity signals, third-party authority) but GEO requires structured, specific, extractable content in a way that standard SEO content does not. A brand can rank well in Google but be absent from AI answers, and vice versa.
Which AI tools does GEO cover?
The main AI answer engines in commercial use today are: Perplexity (live web retrieval), ChatGPT with search/Browse functionality (SearchGPT), Google AI Overviews (appearing above organic results), Google Gemini, Microsoft Copilot, and Claude (Anthropic). Each has slightly different retrieval mechanics, but the underlying GEO principles (entity clarity, structured content, third-party signals) apply across all of them.
Can you measure GEO performance?
GEO is harder to measure than SEO. There is no equivalent of Google Search Console for AI citation frequency. The current best approach is systematic query testing: running a defined set of relevant queries across the main AI tools on a regular basis, recording how and whether the brand appears, and tracking changes over time. A handful of emerging tools are building GEO monitoring capabilities; this is a fast-developing space.
Does AI-generated content help or hurt GEO?
AI-generated content helps GEO when it is well-structured, specific, and clearly attributed. The risk with AI-generated content in a GEO context is generic, vague output that is not useful for AI models to cite. AI-generated product descriptions that include specific attributes, category classifications, and named entities are more GEO-effective than AI-generated generic marketing prose. Structured AI content published under the AI Provenance Protocol also demonstrates the kind of explicit attribution that emerging AI retrieval systems are likely to weight positively.
What is the AI Provenance Protocol and is it relevant to GEO?
The AI Provenance Protocol is an open standard for machine-readable AI content attribution, making it explicitly clear that a piece of content was AI-generated, who generated it, and on what basis. merchi.ai originated the protocol. Its primary purpose is EU AI Act compliance (Article 50 transparency requirements, in force from 2 August 2026). It is not a direct GEO signal, and brands should not conflate the two. For brands publishing AI-generated content at scale, both GEO and compliance are important, but they require different work and different tools.
How long does it take to see GEO results?
Faster than SEO, generally, but with more variability. AI tools with live web retrieval (Perplexity, SearchGPT) can index and cite new content within days of publication. Improvements to entity signals and third-party references take longer to propagate through AI model training cycles. Most brands see measurable improvements in citation frequency within four to eight weeks of systematic GEO work, though this varies significantly by category and the starting point.