LazySEO › Blog › What Is GEO, and How Can Your Company Build a GEO Strategy?

What Is GEO, and How Can Your Company Build a GEO Strategy?

LazySEO · Aug 11, 2026

Generative Engine Optimization (GEO) is the practice of improving how accurately and prominently your company appears in AI-generated answers. A practical GEO strategy has five steps: track real buyer prompts, establish an AI-visibility baseline, improve evidence-rich pages, standardize company information across the web, and measure changes alongside qualified business outcomes.

GEO does not replace SEO. It extends search visibility work into AI experiences such as Google AI Overviews and AI Mode, ChatGPT Search, and Perplexity. The goal is not to force an AI system to mention your brand. The goal is to make your company a credible, retrievable, and accurately represented source for valuable questions.

What is Generative Engine Optimization?

GEO helps companies become discoverable and correctly represented when AI systems answer customer questions. Traditional SEO usually emphasizes rankings, impressions, and clicks in a search-results page. GEO also considers whether a brand is mentioned, whether its pages are cited, how prominently those citations appear, and whether the generated answer is factually accurate.

The term became widely known through the 2023 paper “GEO: Generative Engine Optimization” by Pranjal Aggarwal, Vishvak Murahari, Tanmay Rajpurohit, Ashwin Kalyan, Karthik Narasimhan, and Ameet Deshpande. The work was later published in the *Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining*—KDD ’24. The paper helped define and popularize GEO; it did not establish a universal ranking system or prove that any single optimization method causes citations across all AI platforms. (arxiv.org)

The paper describes a generative engine as a system that can retrieve sources, process or summarize them, and use a language model to produce a response with source attributions. That is a useful research framework, but actual engine behavior differs by product, model, retrieval system, query, location, user context, and date. Query reformulation, source comparison, verification, and synthesis are possible behaviors—not assumptions that should be treated as identical across every AI search experience. (arxiv.org)

GEO metrics are not the same as SEO metrics

A company should distinguish among several related outcomes:

• Visibility: Whether your brand or content appears in an AI-generated answer.
• Mention: A reference to your company, product, people, or category position, whether or not a URL is provided.
• Citation: A linked or otherwise attributed source associated with an answer or claim.
• Attribution: The degree to which the answer connects a specific fact, recommendation, or claim to your company or page.
• Prominence: How much of the answer your source supports and where the citation appears.
• Answer accuracy: Whether the system describes your company, features, pricing, customers, or limitations correctly.
• Referral traffic: Visits that arrive from an AI platform.
• Qualified demand: Leads, product sign-ups, opportunities, revenue, or assisted conversions associated with AI-influenced discovery.

A brand mention can build awareness without producing a visit. A citation can create a path to your website without generating qualified demand. Measure these outcomes separately instead of treating every AI appearance as equivalent to a conversion.

What does the original GEO research show?

The original study introduced GEO-bench, a benchmark containing 10,000 queries from diverse domains and sources. It tested several content-modification methods against a baseline and evaluated visibility using research-specific metrics, including Position-Adjusted Word Count and Subjective Impression. The study reported visibility improvements of up to 40% in its tested generative-engine setting. (arxiv.org)

The researchers also tested a subset of 200 examples using Perplexity. In that experiment, quotation addition produced a 22% improvement over the baseline on Position-Adjusted Word Count. The paper also reported improvements of up to 9% and 37% for other methods on its two reported metrics. Those figures apply to the study’s experimental setup, not to all websites or current versions of Perplexity. (arxiv.org)

The paper separately reported that adding citations, relevant quotations, and statistics produced an increase of more than 40% across various queries. This should not be presented as a universal benchmark or as a single result that applies equally to every method. It refers to the study’s reported experimental comparisons and varies by metric, method, domain, source, and engine. The authors also noted that effectiveness differed across domains and that their work did not evaluate how GEO changes affected ordinary search rankings. (arxiv.org)

Use the research as evidence that structured experimentation is worthwhile—not as a promise of a particular citation rate.

Why should companies invest in GEO?

AI-generated answers can influence category discovery, product research, comparisons, and vendor selection before a prospect visits a company website. The most valuable GEO opportunities are usually high-intent questions where an answer can affect consideration or trust.

GEO can help a company identify:

• Questions where competitors are repeatedly mentioned but your brand is absent.
• Product comparisons where your features or limitations are missing.
• Outdated or incorrect claims about your company.
• Important pages that are not cited even though they contain relevant evidence.
• Third-party profiles that conflict with your current positioning.
• Buyer questions that your website does not answer clearly.

GEO should not be justified by the assumption that all AI answers reduce traffic. For example, an Ahrefs analysis of 300,000 keywords compared informational keywords with and without Google AI Overviews. The analysis estimated that the presence of an AI Overview correlated with a 34.5% reduction in click-through rate for the top-ranking organic page, using a forecasted CTR of 0.040 and an observed CTR of 0.026 for the relevant comparison. The research was observational and based on Ahrefs’ methodology; it does not prove that AI Overviews caused the entire difference or predict results for every site. (ahrefs.com)

The practical implication is to monitor both visibility and demand. A lower click-through rate on an informational query may coexist with more qualified visits from an AI citation—or with no measurable referral at all.

Does GEO replace traditional SEO?

No. SEO remains the technical, content, and authority foundation that helps important pages become discoverable. GEO adds an evaluation layer for AI-generated answers.

Google’s guidance says existing SEO fundamentals remain relevant for its AI features and that there are no additional technical requirements or special AI-only schema needed for eligibility. Pages should still be crawlable, indexable, useful, internally linked, and eligible to appear in search. (developers.google.com)

Maintain these foundations:

• Publish important information in accessible HTML rather than hiding it behind inaccessible scripts, logins, or interfaces.
• Maintain XML sitemaps and clear internal links to product, documentation, comparison, use-case, and methodology pages.
• Keep pages accessible to users and crawlers, with stable URLs and sensible page structure.
• Review robots.txt, firewall, CDN, bot-mitigation, CAPTCHA, authentication, and rate-limit settings.
• Use accurate structured data where it genuinely describes visible page content.
• Monitor indexation and important URL changes in Search Console.

Structured data is useful, but it is not a GEO shortcut

Structured data can help search systems understand what a page contains, but it is not an AI-ranking shortcut and cannot guarantee an AI citation. The markup must accurately represent the visible content on the page. Do not add claims, reviews, prices, dates, or organization details in structured data that users cannot see or verify. Google states that correctly implemented structured data does not guarantee a search appearance. (developers.google.com)

Review crawler access by bot

Crawler policies differ by platform and user agent. OpenAI’s current publisher guidance says that allowing OAI-SearchBot can help public content be discovered and cited in ChatGPT Search, while GPTBot relates to potential training use and OAI-AdsBot serves advertising-related validation. Allowing a crawler does not guarantee that a page will be retrieved, cited, or included in an answer. Coordinate changes with engineering, security, legal, and content teams before changing robots.txt or bot controls. (help.openai.com)

How can your company build a GEO strategy?

Build the program as a repeatable editorial and measurement process rather than a one-time content exercise.

1. Assign ownership and define the business objective

GEO commonly crosses several teams:

• SEO or content strategy: owns prompt research, page priorities, and on-page improvements.
• Product marketing: validates positioning, comparisons, use cases, and competitive claims.
• Subject-matter experts: review technical, legal, financial, medical, or product accuracy.
• PR or digital PR: improves the quality and consistency of third-party references.
• Web and engineering: manages crawlability, structured data, analytics, and access controls.
• Demand generation or revenue operations: connects AI-influenced discovery with leads and pipeline.

Choose one accountable owner. Define whether the first objective is brand accuracy, citation growth, competitive visibility, qualified referrals, or a combination of these.

2. Build a prompt set from real buyer behavior

Start with a manageable set of prompts based on:

• Customer interviews and sales-call questions.
• Site-search data and support tickets.
• Search Console queries and paid-search terms.
• Comparison, alternative, pricing, and implementation searches.
• Questions from review sites, communities, and partner conversations.
• Prompts used by sales representatives during competitive deals.

Include several categories:

• Category and problem-definition prompts.
• “Best,” “top,” and recommendation prompts.
• Alternatives and competitor comparisons.
• Integration, implementation, security, and compliance questions.
• Pricing, contract, and buyer-fit questions.
• Use-case and industry-specific prompts.
• Brand and product fact-check prompts.

Deduplicate prompts by intent. For example, “best project-management software for agencies” and “top project-management tools for creative agencies” may belong to one intent cluster, while “Does Product X integrate with Salesforce?” should remain a separate factual prompt.

3. Establish a baseline before editing pages

A baseline should capture a repeatable sample rather than a single generated answer. For each prompt, record:

• Prompt text and intent category.
• Engine, model, product surface, and account state where known.
• Location, language, device, and date.
• Full answer or a preserved transcript.
• Brand mention: yes, no, or ambiguous.
• Cited URL and citation position.
• Whether the citation supports a central claim or minor detail.
• Competitors mentioned or cited.
• Factual accuracy and sentiment.
• Referral or conversion signal when available.

Use the same prompt wording and environment for the baseline and later comparisons. Run more than one observation per prompt when practical, and report the percentage or range of appearances. Do not treat one answer as a reliable trend.

AI answers can change because of retrieval results, model updates, location, personalization, query wording, and time. Set a minimum observation period—for example, compare repeated samples over several weeks rather than comparing one answer from Monday with one answer from Friday. Track engine and model versions when the platform exposes them, and keep archived transcripts or screenshots so the result can be audited.

4. Prioritize prompts with a commercial-impact score

Do not prioritize prompts only because they have high search volume. Score each prompt from 1 to 5 on:

1. Commercial intent: How close is the prompt to evaluation or purchase?

2. Revenue potential: How valuable is the associated customer segment or use case?

3. Visibility gap: Are competitors present while your company is absent or poorly represented?

4. Factual risk: Could an incorrect answer create legal, compliance, sales, or customer-success problems?

5. Update effort: How difficult is it to create or improve the supporting evidence?

A simple priority score is:

Commercial intent × revenue potential × visibility gap × factual risk ÷ update effort

Use the score to create three queues:

• Fix now: High commercial value, high factual risk, and relatively low effort.
• Build next: High-value gaps requiring new comparison, methodology, or use-case content.
• Monitor: Low-value or low-risk prompts that do not justify immediate editorial work.

5. Improve the pages that should serve as evidence

AI visibility is more defensible when the underlying information is clear, specific, current, and verifiable. Improve pages by:

• Answering the target question near the relevant heading.
• Using descriptive headings and concise paragraphs.
• Defining product capabilities and limitations explicitly.
• Adding comparison tables with consistent criteria.
• Publishing methodology pages for original research, testing, or benchmarks.
• Including dates, assumptions, sample definitions, and source references for important claims.
• Showing authorship, review responsibility, and update dates where appropriate.
• Linking from related product, documentation, and use-case pages.
• Removing duplicated, contradictory, or outdated claims.

Do not add unsupported statistics or generic “authoritative” language. The original GEO study tested statistics, quotations, citations, and other content changes in a controlled setting; it does not justify inserting numbers that your company cannot substantiate. (arxiv.org)

6. Convert an AI-answer defect into an editorial action

Create a defect-to-action workflow. Each issue should produce a specific change, owner, and validation test.

Re-test the same prompt set after the change. If the answer improves, record the change and the observation window; do not assume the improvement will persist indefinitely.

7. Standardize entity information across the web

Entity consistency means that your company is described accurately and consistently across first-party and third-party sources. Audit:

• Your website and documentation.
• Product directories and review profiles.
• Partner and integration pages.
• Industry publications and conference biographies.
• Social profiles and executive bios.
• Press releases and media coverage.
• Business listings and local profiles.

Standardize the company name, product names, official URLs, category description, core capabilities, target customers, ownership, and important limitations. Correct outdated bios, duplicate listings, inconsistent product names, and old feature or pricing claims where possible.

Consistency does not mean publishing identical text everywhere. It means making sure the underlying facts agree.

How should your company measure GEO performance?

Use a scorecard that separates visibility, source quality, answer quality, and business impact.

Recommended GEO scorecard

• Mention rate: Percentage of tracked prompt observations that mention your brand.
• Citation rate: Percentage of observations that cite at least one owned URL.
• Owned citation share: Your owned citations divided by all recorded citations, including competitor citations.
• Citation prominence: Whether your source supports the main recommendation, a major factual claim, or a minor detail.
• Attribution quality: Whether the answer clearly connects the claim to your company or page.
• Answer accuracy: Percentage of observations without material factual errors.
• Factual-risk rate: Percentage of observations containing a potentially consequential error.
• Competitor presence: Frequency and prominence of competitor mentions or citations.
• Sentiment or framing: Positive, neutral, negative, or misleading presentation.
• Prompt coverage: Share of priority prompt clusters where your company appears accurately.
• Referral traffic: Sessions from AI platforms, using analytics referral data where identifiable.
• Business outcomes: Assisted conversions, demo requests, sign-ups, opportunities, pipeline, or revenue linked to AI-influenced journeys.

Track the dimensions separately. A rise in mention rate with no citations may indicate awareness but weak attribution. A rise in citations with poor accuracy may increase visibility while creating operational risk. More referral traffic does not automatically mean more qualified demand.

Handle volatility deliberately

Use sampling rules such as:

• Test a fixed prompt set at a consistent cadence.
• Keep engine, model, location, language, and device consistent where possible.
• Record the date and platform version or surface.
• Deduplicate prompts by intent but preserve meaningful wording variants.
• Run multiple observations for high-priority prompts.
• Compare rolling averages or ranges rather than single answers.
• Preserve answer transcripts and cited URLs.
• Flag major engine or model changes rather than combining them silently with older data.
• Keep a minimum observation period before declaring a trend.

Google reports traffic from its AI features within the overall Web search type in Search Console, so Search Console alone may not isolate AI-feature traffic. Combine it with analytics, conversion data, branded-search trends, CRM attribution, and manual citation observations. (developers.google.com)

A practical 30/60/90-day GEO rollout

Days 1–30: Baseline and risk control

• Assign a GEO owner and supporting stakeholders.
• Define commercial goals and priority markets.
• Build and deduplicate the initial prompt set.
• Test prompts across selected engines and record the baseline.
• Identify factual errors, missing citations, and competitor gaps.
• Audit crawlability, indexation, analytics, structured data, and bot controls.
• Fix high-risk company, product, pricing, and compliance information.

Days 31–60: Evidence-page improvements

• Select the highest-scoring prompt clusters.
• Update one comparison page, one use-case page, one product or documentation page, and one methodology or evidence page.
• Add clear headings, answer-first summaries, tables, source references, dates, and limitations.
• Standardize important entity information across first-party and selected third-party profiles.
• Create defect tickets with owners, due dates, and validation prompts.

Days 61–90: Measurement and expansion

• Re-test the baseline prompt set using the same sampling rules.
• Compare mentions, citations, prominence, accuracy, competitors, and business signals.
• Review whether changes affected conventional SEO performance or conversions.
• Expand to additional engines, markets, languages, and prompt variants only after the initial process is stable.
• Create a monthly operating review for content, product marketing, PR, engineering, and revenue teams.

A GEO platform such as LazySEO can support this process by centralizing prompt monitoring, citations, brand mentions, competitor comparisons, accuracy checks, and trend reporting. The tool should support—not replace—editorial review, source verification, and business attribution.

What are the limitations of GEO?

GEO is an emerging practice, and its measurement environment is unstable. Engines can change retrieval systems, models, citation formats, ranking signals, and access policies without preserving historical behavior. Results can also vary by location, account, device, query wording, and time.

The original GEO paper itself noted that methods may need to adapt as generative engines evolve, that query patterns can change, and that its experiments did not evaluate changes to ordinary search rankings. (arxiv.org)

For that reason, avoid claims such as “this page will always be cited” or “GEO guarantees AI visibility.” A responsible program reports observed changes, explains the sampling method, and connects visibility to accuracy and qualified business outcomes.

FAQ

Is GEO different from SEO?

Yes. SEO focuses primarily on discoverability and performance in conventional search results. GEO focuses on how accurately and prominently a company appears in AI-generated answers, including mentions, citations, attribution, answer accuracy, competitor presence, and downstream demand. GEO depends on many SEO fundamentals rather than replacing them.

How long does GEO take?

There is no reliable universal timeline. A company can establish an initial baseline within a few weeks, but meaningful evaluation requires repeated observations after content, technical, or third-party-profile changes. Use a consistent prompt set and compare trends over several weeks or monthly review periods instead of promising immediate inclusion.

Can GEO results be measured?

Yes, but not with one universal ranking number. Track prompt-level observations such as brand mentions, citation URLs, citation prominence, answer accuracy, competitor presence, sentiment, referral traffic, assisted conversions, and qualified pipeline. Record the engine, model or surface, location, date, prompt, and answer so results remain comparable.

Does GEO guarantee AI citations?

No. No content format, structured-data implementation, crawler setting, or optimization tactic guarantees an AI citation. These actions can improve eligibility, clarity, or the quality of available evidence, but each platform decides what it retrieves and displays.

Should we block or allow AI crawlers?

The decision depends on your organization’s publishing, licensing, privacy, advertising, and training policies. Crawler access policies differ by bot. For OpenAI, OAI-SearchBot is associated with discovery for ChatGPT Search, while GPTBot has separate implications related to potential training use. Review current platform documentation and involve engineering and legal stakeholders before changing access rules. Allowing a crawler does not guarantee citation or inclusion. (help.openai.com)

Can structured data improve GEO?

Accurate structured data can help search systems interpret page content, but it is not a direct AI-ranking shortcut. Markup should match information visible to users, remain complete and current, and follow the relevant platform’s guidelines. Correct markup does not guarantee a rich result, AI citation, or generated-answer inclusion. (developers.google.com)

What should we do when an AI answer is wrong about our company?

Classify the error, identify the page or third-party source that should establish the correct fact, make the editorial or profile update, and re-test the same prompt after an appropriate observation period. For high-risk claims, involve subject-matter, legal, compliance, or product experts and document the correction.

Conclusion

A strong GEO strategy is an operating system for AI-era search visibility. Start with real buyer prompts, establish a repeatable baseline, fix high-risk factual gaps, publish evidence-rich pages, standardize entity information, and measure visibility separately from attribution, traffic, and revenue.

The objective is not to manipulate generated answers or chase isolated mentions. It is to make your company easier to understand, verify, cite, and evaluate—while giving your teams a disciplined way to detect errors, prioritize content work, and connect AI visibility with business results.

References

• https://developers.google.com/search/docs/appearance/ai-features
• https://developers.google.com/search/docs/appearance/ai-features?kgs=aa0bcc3d152ed142
• https://ahrefs.com/blog/ai-overviews-reduce-clicks
• https://www.perplexity.ai/help-center/en/articles/10352895-how-does-perplexity-work