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Key Takeaways
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Generative engine optimization (GEO) is the work of making your brand easy for AI tools to find, cite and describe correctly.
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A generated answer is written before anyone clicks, which means a buyer can form a view of you without visiting your site.
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AI tools assemble their picture of a company from many sources, so inconsistent descriptions across your channels get repeated back.
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Start GEO with a fixed set of buyer questions, run them through the main AI tools, and record how you appear.
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Measure GEO through answer presence, description accuracy, cited sources and AI referral traffic, reviewed against the same questions each month.
Search results used to send B2B buyers to your website to read your answer. Generative engines can now write the answer themselves, and for a growing share of questions they do. GEO is the work that improves the odds of your company appearing in that answer.
What is generative engine optimization (GEO)?
Generative engine optimization (GEO) is the practice of shaping your content and your wider online presence so that generative AI tools mention your brand, cite your pages and describe what you do accurately. The tools in question include ChatGPT, Gemini, Perplexity, Microsoft Copilot and the AI Overviews that Google shows above some search results. Each one writes an answer rather than returning a list of links, drawing on what its model learned in training, on pages it retrieves when the question is asked, or on both.
Search engine optimization aims for a position on a results page. GEO aims for inclusion inside a written answer, which has no fixed positions and changes with the wording of the question.
For a B2B company, GEO covers three jobs: being reachable, so AI tools can read your public pages; being quotable, so your pages state things plainly enough to lift into an answer; and being consistent, so every source tells the same story about what you do and for whom.
What this means for you: treat GEO as an extension of the content and website work you already fund.
Why do B2B brands need generative engine optimization now?
The change is already visible in the software your buyers use at work. Google now shows AI-generated summaries on some results pages, and AI assistants are built into common browsers and office tools.
B2B research questions suit this format well. A question such as "which HubSpot partners work with mid-market manufacturers" or "what should a content audit include" asks for a summary, and a generated answer provides one as a short list of named options. If your company is missing from that list, the buyer has no prompt to look for the page where you explain why you belong on it.
Picture a head of marketing at a 250-person logistics software company who types her own category into an AI assistant out of curiosity. The answer names two competitors and describes her company using a product line it retired two years ago. The outdated description came from an old directory listing nobody had touched since the rebrand, and nothing in her analytics recorded it.
What an AI tool says about you reflects what has been published about you, and each tool refreshes its sources at its own speed. A correction therefore takes time to reach every answer.
What this means for you: run your own category questions through two or three AI tools this week. Note every claim about your company that is missing, outdated or wrong.
How do generative engines decide which brands to mention?
No AI provider publishes a full ranking formula, so any honest answer describes tendencies rather than rules. Tools that retrieve live pages favor content they can reach, parse and match to the question. Tools that rely on training data reflect what was widely published about a company before the model was built.
Three traits make a brand easier to include under either route. The first is a plain, repeated description of what the company does, for whom and in which category. The second is pages that answer specific questions directly. The third is corroboration, meaning other sources such as partner directories, marketplace listings, review profiles and customer case pages describe the company in the same terms.
A company that describes itself four different ways across its homepage, LinkedIn page and listings gives an AI tool four competing versions to choose from. Our guide on why B2B brand identity needs consistent communication covers how to settle that language before you publish more.
Questions to ask: If an AI tool described us today, which source would it most likely draw on? Does that source say what we would want a buyer to hear?
What are the first steps in generative engine optimization?
The first steps cost little. We would typically run them in this order over the first 30 to 60 days.
Build a question set: write 20 to 30 questions a buyer in your category might ask, from early problem questions to direct vendor comparisons. This range is a Markivis starting point, and a narrow niche can work with fewer.
Run a baseline: ask each question in the AI tools your buyers are likely to use. Record whether you appear, how you are described and which sources are cited, and date every entry, since answers change between runs.
The checklist below covers the fixes that follow the baseline. Original material deserves the most effort, since we would expect it to give an AI tool a reason to cite you rather than a competitor. It also ties GEO to your content marketing strategy.
At a glance: a GEO readiness checklist you can work through with your web and content teams.
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Area |
What good looks like |
First step |
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Company description |
One sentence on what you do, for whom and in which category, worded the same everywhere |
Write the sentence and compare it against your homepage, LinkedIn page and listings |
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Question coverage |
A page that directly answers each high-priority buyer question |
Map your question set to existing pages and list the gaps |
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Answer structure |
The answer sits in the first two sentences under a clear heading |
Rewrite the openings of your five highest-value pages |
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Original material |
Frameworks and worked examples that others cannot repeat without citing you |
Choose one internal method and publish it in full |
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Third-party consistency |
Partner directories, marketplaces and review profiles match your own description |
Review every external profile you control and correct the outdated ones |
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Crawler access |
The AI tools you want to appear in can reach your public pages |
Ask your web team to review robots.txt and any bot protection rules |
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Measurement |
A dated baseline for your question set, rerun on a fixed schedule |
Run the question set once and record presence, accuracy and cited sources |
A page can rank on the first results page and still be absent from the generated answer, so ranking reports alone cannot tell you whether buyers are hearing your name.
How do you measure generative engine optimization?
Measure GEO against the question set you built for the baseline, using the same questions and the same tools each time. Four measures give a B2B team a working picture.
Answer presence: the share of your questions where your company appears in the answer.
Description accuracy: whether the answer describes your offer, audience and category correctly. A mark of correct, partly correct or wrong is enough.
Cited sources: which pages each tool links to when it mentions you. Third-party pages here show which profiles deserve attention.
AI referral traffic: visits from domains such as chatgpt.com and perplexity.ai, which web analytics tools can report as referral traffic. Some visits from AI tools arrive without referral data and are counted as direct, so treat the figure as a floor.
A practical starting point is a monthly rerun with a fuller review each quarter. Read the trend across the whole set rather than reacting to a single response. To link GEO to pipeline, add an open "how did you hear about us" field to your demo form and tag every mention of an AI tool.
Questions to ask: Which three questions in our set matter most to revenue, and do we appear in them? Who reviews the monthly results, and what are they allowed to change?
What this means for you: put GEO results into your existing monthly marketing review, next to the metrics that already drive decisions.
The Bottom Line
GEO asks a simple question: when an AI tool explains your category, does it mention you, and does it get you right? Build the question set, run the baseline and fix the description every source should share. The content and consistency work that follows is the same work that makes a B2B brand clearer to human buyers.
The Markivis Approach
Our first GEO session is with the client's sales team rather than its marketers, because salespeople hear the questions buyers ask before a first call. Those questions become the set we run through each AI tool the client's buyers use, and the dated results become the baseline.
From there we treat GEO as positioning and content work with a measurement layer. We agree one company description with product marketing, correct it across owned pages and the external profiles the client controls, and rework the pages an AI tool is most likely to quote, applying principles of answer engine optimisation (AEO). Each month we rerun the same questions and report the results alongside the client's other marketing metrics.
