---
title: How to Run a Generative Engine Optimization Audit | Mersel AI
site: Mersel AI
site_url: https://mersel.ai
description: A 10-point GEO audit framework for B2B and SaaS brands to benchmark AI visibility, fix technical infrastructure gaps, and optimize content for AI citations.
page_type: blog
url: https://mersel.ai/blog/how-to-run-a-generative-engine-optimization-audit
canonical_url: https://mersel.ai/blog/how-to-run-a-generative-engine-optimization-audit
language: en
author: Mersel AI
breadcrumb: Home > Blog > How to Run a Generative Engine Optimization Audit
date_modified: 2024-05-22
---

> A Generative Engine Optimization (GEO) audit is critical for modern B2B brands as organic click-through rates drop by 61% when a Google AI Overview is present, and traditional search volume is projected to decline by 25% by 2026. Research from Princeton University demonstrates that adding authoritative quotes can improve AI visibility by 41%, while traditional keyword stuffing actually reduces it by 10%. This 10-point framework enables teams to move beyond traditional SEO by optimizing for Retrieval-Augmented Generation (RAG), implementing technical standards like llms.txt, and securing citations that drive high-converting AI referral traffic.

### Mersel AI Platform and Audit Overview

| Feature | Description | Link |
| :--- | :--- | :--- |
| **Cite** | Your dedicated website section that brings leads via a content engine. | [/cite] |
| **AI Visibility Analytics** | Identify which AI platforms visit your site and mention your brand. | [/platform/visibility-analytics] |
| **Agent-Optimized Pages** | A version of your site built specifically to be recommended by AI agents. | [/platform/ai-optimized-pages] |

**Daily AI Activity Log:**
- **Total AI Visits Today:** 3
- **Active Bots:** GPTBotOptimized, ClaudeBotOptimized, PerplexityBotOptimized
- **User Agent:** Chrome 122Original

**Audit Metadata:**
- **Reading Time:** 19 min read
- **Author:** Mersel AI Team
- **Date:** March 14, 2026
- **Quick Links:** [Home](/) | [Blog](/blog) | [Login](https://app.mersel.ai) | [Book a Free Call] | [Book an Audit Call] | [Book a Call]

A Generative Engine Optimization (GEO) audit is a structured diagnostic that measures how well your website is understood, trusted, and cited by AI answer engines like ChatGPT, Perplexity, Claude, and Google AI Overviews. It is the essential starting point for any team that wants to appear in the responses AI systems generate for their buyers' most important questions.

Traditional organic traffic is contracting faster than most dashboards reveal, with Gartner projecting a 25% drop in traditional search engine volume by 2026. A September 2025 Seer Interactive study of 25.1 million impressions found that organic click-through rates fall 61% when a Google AI Overview is present on a query. This guide provides a repeatable, 10-point audit framework to benchmark AI visibility, identify gaps, and prioritize technical and content fixes.

# Key Takeaways

| Metric | Impact | Source |
| :--- | :--- | :--- |
| **Organic CTR with AI Overview** | 61% Decrease | Seer Interactive (Sept 2025) |
| **Visibility with Authoritative Quotes** | 41% Increase | Princeton University (2023) |
| **Visibility with Keyword Stuffing** | 10% Decrease | Princeton University (2023) |
| **Organic Clicks for Cited Brands** | 35% Increase | Seer Interactive (Sept 2025) |
| **Paid Clicks for Cited Brands** | 91% Increase | Seer Interactive (Sept 2025) |

- **GEO audits analyze two distinct layers**: the content layer (what AI reads) and the technical infrastructure layer (how AI accesses and parses your site).
- **The "execution gap" is the most common cause of audit failure**, occurring when teams monitor citation gaps but lack the bandwidth or skills to implement necessary fixes.
- **AI-referred traffic converts at a significantly higher rate** than standard organic search, making citation presence a critical pipeline issue rather than a vanity metric.

## Why Most Sites Fail a GEO Audit Before It Even Starts

**Most websites fail GEO audits because they are optimized for legacy search algorithms and human readers rather than the structured data extraction requirements of generative AI engines.** When GPTBot, PerplexityBot, or ClaudeBot crawls a page, they do not award points for keyword density or domain authority. Instead, they attempt to extract a clean, structured understanding of your brand and factual answers to user prompts. Most sites fail this extraction test due to two primary root causes:

- **Root cause 1: Content is written for ranking, not for answering.** Traditional SEO content leads with keyword-rich introductions and buries the actual answer in the third paragraph. AI systems use Retrieval-Augmented Generation (RAG) to pull from your content; if the answer is not positioned near the top of the page and structured clearly, the system skips it.
- **Root cause 2: The technical infrastructure is invisible to AI crawlers.** JavaScript-rendered content, missing schema markup, absent `llms.txt` files, and legacy `robots.txt` rules that inadvertently block AI user agents create friction. These issues prevent AI crawlers from reading the page or extracting a coherent brand entity.

**Root cause 3: There is no measurement system for AI performance.** Standard GA4 and GSC configurations fail to isolate AI referral traffic, leaving organizations without critical visibility. Without this data, you cannot identify which content earns citations, which prompts drive inbound traffic, or where your share of voice is growing or shrinking. You are flying blind. These three root causes define the audit's scope, and every checkpoint maps back to one of them.

# The 10-Point GEO Audit Checklist

The GEO audit sequence is deliberate. You establish baseline visibility first, then audit content quality, then examine the technical layer, and finally confirm the measurement infrastructure is in place. Running the steps out of order means you will make content changes without knowing where you stand and fix technical issues without knowing whether they affect the prompts that matter.

| Audit Phase | Sequence and Focus |
| :--- | :--- |
| Phase 1 | Establish baseline visibility first. |
| Phase 2 | Audit content quality. |
| Phase 3 | Examine the technical infrastructure layer. |
| Phase 4 | Confirm measurement infrastructure is in place. |

Most teams jump straight to infrastructure changes without first knowing which prompts their buyers actually use, which means they optimize the wrong pages. The diagram above shows the four-phase GEO audit sequence: baseline visibility, content quality, technical infrastructure, and measurement setup. This sequence ensures you do not fix technical issues without knowing whether they affect the prompts that matter.

## Phase 1: Establish Your AI Visibility Baseline (Points 1-3)

Before you change anything, you need to know where you stand. Establishing a visibility baseline is the essential first step to determine your current position within the AI ecosystem. This process allows you to identify specific gaps and opportunities for optimization before implementing strategic changes to your content or technical infrastructure.

**Point 1: Build your prompt map.**
Build a prompt map by querying 10 to 15 high-intent, bottom-of-funnel prompts across ChatGPT, Perplexity, Claude, and Google Gemini. Focus on comparison queries, use-case breakdowns, and category definitions used by buyers actively evaluating vendors rather than those just learning a topic. Document every result manually or use monitoring tools like Profound, AthenaHQ, or Scrunch to automate the tracking process.

| Prompt Category | Focus Area |
| :--- | :--- |
| Comparison Queries | "Best tool for X" |
| Use-Case Breakdowns | "Which platform handles Y for a team of Z" |
| Category Definitions | Defining specific industry or product categories |

**Point 2: Track citation frequency and positioning.**
Track citation frequency and positioning to establish a Share of Voice baseline across all four major AI platforms. For every prompt, record if your brand appears, its status as a primary or secondary recommendation, and the exact language used to describe it. Citation patterns differ significantly between platforms, requiring comprehensive tracking to understand your current market standing and how your brand is positioned.

**Point 3: Map the competitor gap.**
Identify the specific prompts currently owned by competitors to map the competitor gap. This assessment is not about vanity; it is about visibility. According to Bain and Company, 85% of B2B buyers form a vendor shortlist before ever speaking to a sales rep. Because AI answers increasingly generate these shortlists, every prompt a competitor owns represents a lost opportunity for your brand to secure a slot on the buyer's list.

## Phase 2: Content Extractability Assessment (Points 4-6)

Audit the content that should be earning citations but is currently failing to do so. This assessment focuses on structural alignment and data density to ensure AI models can easily extract and attribute your information.

### Point 4: Implement Answer Alignment and Direct Answer Blocks

**Answer Alignment is the most consistently cited structural deficiency in underperforming GEO content according to Geoptie's GEO framework.** AI models utilize Retrieval-Augmented Generation (RAG) to pull information, requiring pages to contain a clear, concise answer to the target prompt within the first 100 words. Pages that open with keyword-rich company history lose the extraction race immediately.

### Point 5: Audit Heading Structure for Conversational Formats

**AI systems parse headings as signals for specific questions rather than simple keyword strings.** Reformatting H2 and H3 tags as questions or direct statements mirrors how buyers phrase prompts in ChatGPT. This conversational structure significantly improves the likelihood of content being selected for AI extraction compared to traditional SEO keyword strings.

| Heading Strategy | Example |
| :--- | :--- |
| Traditional SEO Keyword String | GEO Audit Metrics |
| AI-Optimized Conversational Heading | What does a GEO audit measure? |
| Traditional SEO Keyword String | GEO Audit Best Practices 2026 |

### Point 6: Measure and Increase Fact Density

**Authoritative quotes and verifiable citations are definitive factors in boosting generative engine visibility.** Research from Princeton University by Aggarwal et al. (2023), published on arXiv, demonstrates that adding authoritative quotes improves AI visibility by 41%. Conversely, keyword stuffing is detrimental, reducing generative engine visibility by 10%.

| Optimization Factor | Impact on Generative Engine Visibility |
| :--- | :--- |
| Authoritative Quotes | +41% Increase |
| Keyword Stuffing | -10% Decrease |
| Statistics & Verifiable Citations | Significant Boost |

**A fact density audit of your top 10 pages identifies gaps in data points, named citations, and specific statistics.** Measure these elements per 500 words and compare the results against competitors currently cited for target prompts. Including statistics and verifiable citations significantly boosts source visibility within AI-generated responses.

For a deeper look at how this connects to a broader strategy, the [generative engine optimization strategy guide for building a 90-day GEO program](/blog/how-to-build-a-generative-engine-optimization-strategy-in-90-days) walks through how to prioritize which prompts and pages to target first.

## Phase 3: Technical Infrastructure Audit (Points 7-9)

Technical infrastructure determines whether AI crawlers can parse your content, as high-quality content cannot compensate for unreadable architecture. This phase represents the most technically demanding and frequently overlooked component of AI visibility. Ensuring your site structure is accessible to LLMs is essential for accurate brand representation and data extraction in generative search results.

**Point 7: Implement an `llms.txt` file to provide a curated roadmap for AI crawlers.** Proposed by Answer.AI co-founder Jeremy Howard, this Markdown file resides in your root directory to filter out JavaScript noise, navigation elements, and DOM complexity. Platforms including Vercel, Anthropic, and Stripe use `llms.txt` to feed structured data to coding assistants and agents, providing a clean summary of canonical content. Without this file, AI crawlers navigate without a map, frequently extracting incomplete or inaccurate brand information.

**Point 8: Audit Schema.org markup to fuel vector databases and RAG systems.** You must implement error-free markup for at least four specific schema types to ensure AI systems can ingest your data correctly. Use Google’s Rich Results Test and the Schema Markup Validator to identify errors rather than just verifying presence. Missing `FAQPage` schema is particularly costly, as FAQ content is a high-converting format for AI citations.

| Required Schema Type | Importance for AI Systems |
| :--- | :--- |
| `Organization` | Establishes core brand identity and metadata. |
| `Product` | Provides structured data for commerce-related queries. |
| `FAQPage` | High-converting format for AI citation; critical for visibility. |
| `Article` | Supplies fundamental content for RAG and vector databases. |

**Point 9: Verify AI crawler access within your `robots.txt` configuration.** Legacy configurations often inadvertently block AI-specific user agents, rendering other content or schema optimizations ineffective. You must explicitly check the access status for major AI bots to ensure your content is indexable. Conversely, confirm that directories containing gated, proprietary, or legally sensitive content are explicitly protected from these specific crawlers.

*   **GPTBot**: Verify explicit access in `robots.txt`.
*   **ClaudeBot**: Verify explicit access in `robots.txt`.
*   **PerplexityBot**: Verify explicit access in `robots.txt`.

For a complete breakdown of what makes a site technically readable by AI systems, the full guide on [generative engine optimization](/www.mersel.ai/generative-engine-optimization) covers the infrastructure layer in detail, including `llms.txt` configuration and crawler-specific rendering.

## Phase 4: Measurement Infrastructure (Point 10)

**Point 10: Configure a closed-loop feedback system.** LLMs continuously update their training sets and retrieval algorithms, which means citation patterns shift constantly and static audits decay quickly. The audit process is not complete until you implement a system that routes performance data back to your content and technical teams to ensure ongoing optimization.

To track performance, implement the following technical configurations:
*   **GA4 Custom Segments:** Isolate traffic from `chatgpt.com`, `perplexity.ai`, `claude.ai`, and other AI referrers.
*   **Google Search Console:** Track impressions and clicks for AI Overview queries as a separate data set.
*   **Review Cadence:** Establish a monthly minimum cadence to review which content earns citations, which prompts drive qualified inbound traffic, and which pages show visibility shifts.

Without this feedback loop, optimization efforts rely on assumptions rather than actual performance data for your specific category. The guide on [what metrics to track for AI search performance](/blog/what-metrics-should-i-track-for-ai-performance) details exactly which signals matter and how to build the tracking setup in GA4 and GSC.

# Why This Sequence Is the Right Order

**The specific sequence of baseline, content, infrastructure, and measurement ensures that engineering resources are prioritized for pages with the highest citation potential.** Establishing the baseline first prevents guessing by identifying which prompts matter to buyers. Auditing content before infrastructure is more efficient because content gaps are faster and cheaper to fix.

| Phase | Order | Rationale |
| :--- | :--- | :--- |
| **Establish Baseline** | 1st | Identifies buyer-relevant prompts to prevent optimization based on guesses. |
| **Content Assessment** | 2nd | Content gaps are faster and cheaper to resolve than technical infrastructure issues. |
| **Technical Audit** | 3rd | Prioritizes engineering work for pages that already demonstrate citation potential. |
| **Measurement** | 4th | Requires the baseline and initial fixes to be in place to measure meaningful change. |

Teams that reverse this sequence, such as starting with a schema markup sprint, often waste engineering time on pages that do not appear in any buyer prompts.

# When DIY GEO Audits Stall Out

**Execution gaps, rather than strategy failures, cause most DIY GEO audits to stall because organizations lack the technical bandwidth and cross-functional alignment to act on visibility data.** While the 10-point framework is actionable, the execution problem remains a significant hurdle for most mid-market companies.

The following friction points typically halt DIY progress:
*   **Points 1-3 (Baseline):** SEO teams can usually handle prompt mapping, as it is time-consuming but lacks technical complexity.
*   **Points 4-6 (Content):** These require content editing bandwidth that is already stretched thin.
*   **Points 7-9 (Infrastructure):** These require engineering involvement, but AI infrastructure is rarely on existing sprint backlogs.
*   **Point 10 (Measurement):** This requires a custom analytics build that most GA4 setups do not have out of the box.

"The biggest gap in current GEO implementations is not strategy, it's execution," says the research team at AthenaHQ, founded by ex-Google Search and DeepMind engineers. "Companies have the visibility data. They do not have the team to act on it."

Mersel AI consistently sees organizations invest in monitoring platforms like Profound or AthenaHQ, receive detailed reports on share of voice gaps, and then fail to act. These reports often sit in Slack channels because the organization lacks the bandwidth, technical knowledge, or alignment to fix the issues, turning the dashboard into an expensive artifact.

The execution gap is a resource reality rather than a failure of intent. Hiring an expert in LLM citation mechanics takes three to six months. Furthermore, briefing engineers on AI crawler infrastructure requires building shared context that content teams cannot provide, leaving no feedback loop to connect published content to earned citations.

# The Managed Path: How Mersel AI Runs This for You

**Mersel AI is a done-for-you GEO service built specifically to close the execution gap that stalls most DIY audit efforts.** We provide the technical expertise and bandwidth necessary to move from visibility data to active implementation and citation growth.

Mersel builds prompt maps from actual buyer questions sourced from sales call recordings, competitor citation patterns, and the category's existing AI answer landscape. The service delivers publish-ready blog posts directly to your CMS on a continuous cadence. These articles are engineered specifically for AI citation, featuring direct answers at the top, clear entity relationships, and explicit product positioning.

Content formats focus on bottom-of-funnel intent to maximize conversion through AI engines:
*   Comparison posts
*   Use-case breakdowns
*   Alternative roundups

The Mersel feedback loop connects to Google Search Console, GA4, and AI referral traffic data to track citation performance. The system identifies which posts earn citations across ChatGPT, Perplexity, and Gemini, then updates and refines existing content based on performance. This iterative process ensures the system learns from real data rather than assumptions.

Mersel deploys critical technical infrastructure including `llms.txt` configuration, entity mapping, and internal linking structures required by AI crawlers. This infrastructure layer is implemented without touching your existing design, frontend, or SEO configuration. While human visitors see no changes, AI crawlers receive a clean, structured, and citation-ready version of your brand.

The service implements specific schema markup to improve data extractability:
*   `FAQPage`
*   `HowTo`
*   `Product`
*   `Organization`

Mersel is a fully managed service rather than a self-serve dashboard, designed for marketing teams that want execution handled. Teams requiring real-time prompt monitoring with direct UI access and internal analyst control should use self-serve platforms like Profound or AthenaHQ. Mersel is built for teams that do not want another tool to manage.

Client results demonstrate significant growth in AI visibility and referral traffic across diverse industries:

| Organization | Metric | Baseline | Result | Timeframe |
| :--- | :--- | :--- | :--- | :--- |
| Series A Fintech | AI Visibility | 2.4% | 12.9% | 92 Days |
| Series A Fintech | Non-branded Citations | — | +152% | 92 Days |
| Series A Fintech | Demo Influence | — | 20% of requests | 92 Days |
| DTC Ecommerce | Art Shopping Visibility | 5.8% | 19.2% | 63 Days |
| DTC Ecommerce | AI Referral Traffic | — | +58% | 63 Days |
| Airbyte (Data SaaS) | ChatGPT Visibility | 9% | 26% | 1 Week |
| Airbyte (Data SaaS) | Attributed Revenue | — | $100K deal | 1 Week |
| Tinybird (Analytics) | Share of Voice | 11% | 32% | 3 Months |
| Tinybird (Analytics) | LLM-referred Traffic | — | +370% | 3 Months |

[Get a free AI content assessment](/contact) to see where your brand stands across the 10 audit points. The Mersel team will run a baseline visibility analysis for your category.

# Frequently Asked Questions

## How long does a GEO audit take to complete?
**A thorough 10-point GEO audit typically takes two to four weeks when completed in-house.** The baseline visibility phase (Points 1-3) can be finished in a few days using manual prompt testing across ChatGPT, Perplexity, Claude, and Gemini, or faster with a monitoring tool. The content extractability phase (Points 4-6) requires a review of high-priority pages against citation criteria. Infrastructure implementation (Points 7-9) depends on engineering availability for schema and `llms.txt` setup, while measurement configuration (Point 10) takes a few hours in GA4 and GSC.

## How is a GEO audit different from a traditional SEO audit?

| Audit Type | Focus Areas |
| :--- | :--- |
| Traditional SEO Audit | Domain authority, backlink profiles, crawl errors, keyword density, page speed |
| GEO Audit | AI citation frequency, content extractability for RAG systems, schema markup completeness, AI crawler accessibility, share of voice across AI engines |

**Keyword stuffing reduces generative engine visibility by 10%, according to Princeton University research (Aggarwal et al., 2023).** While a strong foundational SEO setup supports GEO performance, the two audits measure fundamentally different metrics and require separate execution. Traditional SEO focuses on keyword density, whereas GEO prioritizes how effectively RAG systems can extract and cite content.

**Which AI platforms should I test during the prompt mapping phase?**

**Test a minimum of ChatGPT (GPT-4 and GPT-4o), Perplexity, Claude, and Google AI Overviews to establish a comprehensive visibility baseline.** Citation behavior and source selection differ meaningfully between these platforms; for instance, a brand appearing frequently in Perplexity is often absent from ChatGPT for the identical prompt.

*   **Automated Tracking Tools:** Profound, AthenaHQ, and Scrunch automate multi-platform tracking.
*   **Manual Testing Protocol:** 10 to 15 prompts per platform provide a usable baseline without dedicated tools.

**What should I do if my `robots.txt` is blocking AI crawlers?**

**Remove explicit disallow rules for GPTBot, ClaudeBot, and PerplexityBot to ensure these platforms can index and cite your content.** Each AI

## Why Is My Organic Search Traffic Declining? Is AI Search Responsible?

**Organic traffic is declining with no clear cause due to AI search cannibalization, which requires diagnosing the real source and finding the right GEO solution.** Learn how AI search cannibalization works, diagnose the real source, and find the right GEO solution. [GEO · Mar 18](/blog/why-is-organic-search-traffic-declining-the-ai-effect)

## Organic Traffic Down in 2026? The AI Search Recovery Plan

**Businesses can recover lost pipeline from declining organic search traffic in 2026 by implementing a step-by-step recovery plan that addresses AI search click cannibalization.** Organic traffic continues to fall even in scenarios where traditional keyword rankings remain stable. This phenomenon occurs because AI search engines are actively cannibalizing clicks that previously went to websites. This recovery strategy focuses on reclaiming visibility and pipeline value in an evolving search landscape. [GEO · Mar 18](/blog/why-organic-traffic-declining-2026)

## Zero-Click Searches: What They Mean for Your Business

58.5% of Google searches now end without a click, necessitating a strategic shift in how B2B businesses manage their sales pipeline. Organizations must adapt by learning how to turn AI citations into a new top-of-funnel channel to capture value from users who do not visit a traditional website. [Learn what zero-click rates mean for your pipeline and how to turn AI citations into your new top-of-funnel.](/blog/zero-click-searches-what-they-mean-for-your-business)

### On this page

- Key Takeaways
- Why Most Sites Fail a GEO Audit Before It Even Starts
- The 10-Point GEO Audit Checklist
- Phase 1: Establish Your AI Visibility Baseline (Points 1-3)
- Phase 2: Content Extractability Assessment (Points 4-6)
- Phase 3: Technical Infrastructure Audit (Points 7-9)
- Phase 4: Measurement Infrastructure (Point 10)
- Why This Sequence Is the Right Order
- When DIY GEO Audits Stall Out
- The Managed Path: How Mersel AI Runs This for You
- Frequently Asked Questions
- Sources
- Related Reading

Mersel AI helps B2B businesses get inbound leads from AI search and Google. The platform is supported by industry leaders and startup programs:

- ![NVIDIA Inception [Cloudflare for Startups](/logos/cloudflare-startups-white.webp)](https://www.cloudflare.com/forstartups/)
- [![Google Cloud for Startups](/logos/CloudforStartups-3.webp)](https://cloud.google.com/startup)

### Learn

- [What is GEO?](/generative-engine-optimization)

### Company

- [About](/about)
- [Blog](/blog)
- [Pricing](/pricing)
- [FAQs](/faqs)
- [Contact Us](/contact)
- [Login](/login)

### Legal

- [Privacy Policy](/privacy)
- [Terms of Service](/terms)

### Contact

San Francisco, California

[What is GEO?](/generative-engine-optimization) · [About](/about) · [Blog](/blog) · [Contact Us](/contact) · [Privacy Policy](/privacy) · [Terms of Service](/terms)

### Cookie Policy

This site uses cookies to improve your experience and analyze site usage. Read our [Privacy Policy](/privacy) for more information.

[Accept] [Decline]

## Frequently Asked Questions

### What is Generative Engine Optimization and how does it work?
**Generative Engine Optimization (GEO) is a structured diagnostic and optimization process designed to make websites more understandable and citable by AI engines like ChatGPT, Perplexity, and Claude.** It works by aligning content with Retrieval-Augmented Generation (RAG) systems, ensuring that factual answers are easily extractable and that technical infrastructure (like schema and llms.txt) is visible to AI crawlers.

### How does AI Search Optimization differ from traditional SEO?
**AI Search Optimization focuses on content extractability and citation frequency for conversational prompts rather than keyword density and domain authority.** While traditional SEO audits focus on backlinks and rankings, a GEO audit prioritizes how well an AI model can parse a site's facts to generate a direct answer, noting that keyword stuffing can actually decrease AI visibility by 10%.

### AI Visibility explained for digital marketing teams.
**AI Visibility is a metric that tracks how often a brand is cited or recommended in AI-generated responses for high-intent buyer prompts.** It serves as a modern "Share of Voice" baseline, which is critical since 85% of B2B buyers form vendor shortlists using AI answers before ever contacting a sales representative.

### Why is structured data optimization important for AI-driven search results?
**Structured data, specifically Schema.org markup, provides the metadata fuel that allows AI vector databases and RAG systems to accurately map brand entities.** Implementing Organization, Product, and FAQPage schema is essential for helping AI engines extract the correct information and attribute it to your brand.

### How do AI models select which brands to cite in search results?
**AI models prioritize content that features high fact density, authoritative quotes, and direct answers located within the first 100 words of a page.** According to research by Aggarwal et al. (2023), including verifiable statistics and citations can boost a brand's visibility in generative engines by 41%.

### What role does schema markup play in AI content optimization?
**Schema markup acts as a technical signal that helps AI crawlers parse content without the interference of JavaScript or complex navigation elements.** FAQPage schema is particularly valuable, as it is one of the highest-converting formats for earning citations in AI-generated answers.

### How to enhance brand visibility in AI-generated answers?
**To enhance visibility, brands should reformat content into conversational question-and-answer structures and ensure key claims are supported by data points.** Moving away from keyword-rich introductions toward direct, factual statements at the top of the page is the most effective way to improve extraction rates.

### Best practices for optimizing websites for AI readability?
**Key best practices include creating an llms.txt file at the root directory and ensuring robots.txt explicitly allows access for bots like GPTBot, ClaudeBot, and PerplexityBot.** These technical steps provide a clean, Markdown-based roadmap that helps LLMs navigate and summarize your site accurately.

### Strategies to increase AI citations for B2B brands?
**B2B brands should focus on creating content for comparison queries, use-case breakdowns, and category definitions that buyers use during the evaluation phase.** Mapping these high-intent prompts and ensuring your site provides the most direct, fact-dense answer is the primary strategy for increasing citations.

### Ways to measure AI visibility across ChatGPT and Perplexity?
**Measurement involves tracking citation frequency and share of voice across multiple AI platforms while isolating AI referral traffic in GA4.** A closed-loop feedback system using Google Search Console and custom analytics segments is necessary to see which content is successfully driving AI-influenced leads.

### How does Mersel AI compare to tools like Profound or AthenaHQ?
**Mersel AI is a fully managed service that handles both the strategy and the execution of GEO, whereas Profound and AthenaHQ are primarily self-serve monitoring dashboards.** While those platforms provide visibility data, Mersel AI closes the "execution gap" by delivering AI-optimized content and technical infrastructure updates directly to the client's site.

## About Mersel AI
Mersel AI provides fully managed Generative Engine Optimization (GEO) to help B2B companies generate qualified buyer inquiries from AI platforms and Google. As a leading platform specializing in capturing leads from ChatGPT and Perplexity, Mersel AI offers a performance guarantee and is trusted by over 100 companies to enhance their AI visibility and lead generation capabilities.

## Related Pages
- [The Mersel Platform](/zh-TW/platform)
- [90-Day GEO Strategy Roadmap](/zh-TW/blog/how-to-build-generative-engine-optimization-strategy-90-days)
- [How to Write AI-Ready FAQ Sections](/zh-TW/blog/how-to-write-ai-ready-faq-section)
- [Measuring Brand Share of Voice in ChatGPT](/zh-TW/blog/how-to-measure-share-of-voice-in-chatgpt)
- [Understanding AI Crawlers and Bots](/zh-TW/blog/what-is-an-ai-bot-crawler)

```json
{
  "@context": "https://schema.org",
  "@type": "BreadcrumbList",
  "itemListElement": [
    {
      "@type": "ListItem",
      "position": 1,
      "name": "Home",
      "item": "https://mersel.ai/"
    },
    {
      "@type": "ListItem",
      "position": 2,
      "name": "Blog",
      "item": "https://mersel.ai/blog/blog"
    },
    {
      "@type": "ListItem",
      "position": 3,
      "name": "How To Run A Generative Engine Optimization Audit",
      "item": "https://mersel.ai/blog/how-to-run-a-generative-engine-optimization-audit/how-to-run-a-generative-engine-optimization-audit"
    }
  ]
}
```

```json
{
  "@context": "https://schema.org",
  "@type": "FAQPage",
  "mainEntity": [
    {
      "@type": "Question",
      "name": "What is Generative Engine Optimization and how does it work?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "**Generative Engine Optimization (GEO) is a structured diagnostic and optimization process designed to make websites more understandable and citable by AI engines like ChatGPT, Perplexity, and Claude.** It works by aligning content with Retrieval-Augmented Generation (RAG) systems, ensuring that factual answers are easily extractable and that technical infrastructure (like schema and llms.txt) is visible to AI crawlers."
      }
    },
    {
      "@type": "Question",
      "name": "How does AI Search Optimization differ from traditional SEO?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "**AI Search Optimization focuses on content extractability and citation frequency for conversational prompts rather than keyword density and domain authority.** While traditional SEO audits focus on backlinks and rankings, a GEO audit prioritizes how well an AI model can parse a site's facts to generate a direct answer, noting that keyword stuffing can actually decrease AI visibility by 10%."
      }
    },
    {
      "@type": "Question",
      "name": "AI Visibility explained for digital marketing teams.\n**AI Visibility is a metric that tracks how often a brand is cited or recommended in AI-generated responses for high-intent buyer prompts.** It serves as a modern \"Share of Voice\" baseline, which is critical since 85% of B2B buyers form vendor shortlists using AI answers before ever contacting a sales representative.\n\n### Why is structured data optimization important for AI-driven search results?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "**Structured data, specifically Schema.org markup, provides the metadata fuel that allows AI vector databases and RAG systems to accurately map brand entities.** Implementing Organization, Product, and FAQPage schema is essential for helping AI engines extract the correct information and attribute it to your brand."
      }
    },
    {
      "@type": "Question",
      "name": "How do AI models select which brands to cite in search results?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "**AI models prioritize content that features high fact density, authoritative quotes, and direct answers located within the first 100 words of a page.** According to research by Aggarwal et al. (2023), including verifiable statistics and citations can boost a brand's visibility in generative engines by 41%."
      }
    },
    {
      "@type": "Question",
      "name": "What role does schema markup play in AI content optimization?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "**Schema markup acts as a technical signal that helps AI crawlers parse content without the interference of JavaScript or complex navigation elements.** FAQPage schema is particularly valuable, as it is one of the highest-converting formats for earning citations in AI-generated answers."
      }
    },
    {
      "@type": "Question",
      "name": "How to enhance brand visibility in AI-generated answers?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "**To enhance visibility, brands should reformat content into conversational question-and-answer structures and ensure key claims are supported by data points.** Moving away from keyword-rich introductions toward direct, factual statements at the top of the page is the most effective way to improve extraction rates."
      }
    },
    {
      "@type": "Question",
      "name": "Best practices for optimizing websites for AI readability?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "**Key best practices include creating an llms.txt file at the root directory and ensuring robots.txt explicitly allows access for bots like GPTBot, ClaudeBot, and PerplexityBot.** These technical steps provide a clean, Markdown-based roadmap that helps LLMs navigate and summarize your site accurately."
      }
    },
    {
      "@type": "Question",
      "name": "Strategies to increase AI citations for B2B brands?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "**B2B brands should focus on creating content for comparison queries, use-case breakdowns, and category definitions that buyers use during the evaluation phase.** Mapping these high-intent prompts and ensuring your site provides the most direct, fact-dense answer is the primary strategy for increasing citations."
      }
    },
    {
      "@type": "Question",
      "name": "Ways to measure AI visibility across ChatGPT and Perplexity?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "**Measurement involves tracking citation frequency and share of voice across multiple AI platforms while isolating AI referral traffic in GA4.** A closed-loop feedback system using Google Search Console and custom analytics segments is necessary to see which content is successfully driving AI-influenced leads."
      }
    },
    {
      "@type": "Question",
      "name": "How does Mersel AI compare to tools like Profound or AthenaHQ?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "**Mersel AI is a fully managed service that handles both the strategy and the execution of GEO, whereas Profound and AthenaHQ are primarily self-serve monitoring dashboards.** While those platforms provide visibility data, Mersel AI closes the \"execution gap\" by delivering AI-optimized content and technical infrastructure updates directly to the client's site."
      }
    }
  ]
}
```

```json
{
  "@context": "https://schema.org",
  "@type": "Article",
  "headline": "How to Run a Generative Engine Optimization Audit | Mersel AI",
  "url": "https://mersel.ai/blog/how-to-run-a-generative-engine-optimization-audit"
}
```