---
title: How to Run a Generative Engine Optimization Audit | Mersel AI
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description: A 10-point GEO audit framework for SEO leaders to benchmark AI visibility, fix content extractability, and close technical gaps costing citations.
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date_modified: 2025-05-22
---

> Traditional search engine volume is projected to drop 25% by 2026, and organic CTR already falls by 61% when Google AI Overviews are present. To maintain visibility, brands must optimize for AI citation, as adding authoritative quotes improves AI visibility by 41% while keyword stuffing reduces it by 10%. Brands cited in AI Overviews earn 35% more organic clicks and 91% more paid clicks, making GEO a critical pipeline driver. Using Mersel AI's framework, a Series A fintech startup grew AI visibility from 2.4% to 12.9% in just 92 days.

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# How to Run a Generative Engine Optimization Audit
19 min read | Mersel AI Team | March 14, 2026
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**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 serves as 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 search engine volume is projected by Gartner to drop 25% 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. If you lead SEO for a B2B or SaaS brand, this guide gives you a repeatable, 10-point audit framework to benchmark your current AI visibility, identify gaps, and prioritize fixes.

# Key Takeaways

| Metric or Category | Key Finding | Source / Context |
| :--- | :--- | :--- |
| **Organic CTR Impact** | 61% decrease when Google AI Overview is present | Seer Interactive (Sept 2025) |
| **Authoritative Quotes** | Improves AI visibility by 41% | Princeton University (Aggarwal et al., 2023) |
| **Keyword Stuffing** | Reduces AI visibility by 10% | Princeton University (Aggarwal et al., 2023) |
| **Organic Click Lift** | Cited brands earn 35% more organic clicks | Seer Interactive Study |
| **Paid Click Lift** | Cited brands earn 91% more paid clicks | Seer Interactive Study |
| **Audit Scope** | Covers content layer (what AI reads) and technical infrastructure layer | Mersel AI Framework |
| **Primary Failure** | "Execution gap" where teams monitor but do not fix gaps | Operational Audit Insight |
| **Conversion Rate** | AI-referred traffic converts significantly higher than standard organic search | Performance Metric |

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

**Most websites fail GEO audits because they are optimized for human readers and legacy search algorithms rather than the structured data extraction requirements of generative engines.** When GPTBot, PerplexityBot, or ClaudeBot crawl 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 whether your content provides direct, factual answers to user prompts. Most websites fail that extraction test because of three root causes:

1.  **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 near the top of the page and structured clearly, it gets skipped.
2.  **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 block AI user agents all create friction. These issues prevent AI crawlers from reading the page or extracting a coherent brand entity from it.

**Root cause 3: There is no measurement system for AI performance.** Most GA4 and GSC setups are not configured to isolate AI referral traffic. Without that data, you cannot see which content earns citations, which prompts drive inbound, or where your share of voice is growing or shrinking; essentially, you are flying blind. These three root causes define the audit's scope, and every checkpoint below maps back to one of them.

# The 10-Point GEO Audit Checklist

The 10-point GEO audit follows a deliberate sequence to ensure optimization is data-driven and effective. You must establish baseline visibility first, then audit content quality, examine the technical layer, and finally confirm the measurement infrastructure. Running these steps out of order leads to making content changes without situational awareness and fixing technical issues that may not affect the prompts that matter.

The four-phase GEO audit sequence consists of:
*   **Phase 1:** Baseline visibility
*   **Phase 2:** Content quality
*   **Phase 3:** Technical infrastructure
*   **Phase 4:** Measurement setup

*The diagram above shows the four-phase GEO audit sequence: baseline visibility, content quality, technical infrastructure, and measurement setup. Most teams jump straight to infrastructure changes without first knowing which prompts their buyers actually use, which means they optimize the wrong pages.*

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

**Bain and Company research indicates that 85% of B2B buyers form a vendor shortlist before ever speaking to a sales representative.** Because AI answer engines are increasingly where these shortlists are constructed, brands must establish a visibility baseline before implementing changes. This phase identifies your current standing across the major generative platforms.

**Point 1: Build your prompt map.** Query 10 to 15 high-intent, bottom-of-funnel prompts across major AI platforms. Focus on comparison queries, use-case breakdowns, and category definitions that buyers use when actively evaluating vendors. These prompts target users seeking specific solutions rather than general information.

| Audit Element | Details |
| :--- | :--- |
| **Target Platforms** | ChatGPT, Perplexity, Claude, Google Gemini |
| **Query Types** | "Best tool for X", "Which platform handles Y for a team of Z", Category definitions |
| **Tracking Methods** | Manual documentation or automation tools (Profound, AthenaHQ, Scrunch) |

**Point 2: Track citation frequency and positioning.** Record whether your brand appears for each prompt, its status as a primary recommendation or secondary mention, and the exact language used. This data establishes your Share of Voice baseline. Perform this tracking across all four major AI platforms because citation patterns differ significantly between them.

**Point 3: Map the competitor gap.** Identify specific prompts currently owned by competitors to determine where your brand is missing from the buyer's shortlist. This assessment is a strategic necessity rather than a vanity metric. Every prompt owned by a competitor represents a shortlist slot your brand does not occupy during the AI-driven research phase.

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

Audit the specific content that should be earning citations but is currently failing to appear in generative AI responses.

**Point 4: Check for direct answer blocks.**

**Direct answer blocks must appear within the first 100 words of your most important pages to ensure AI models can pull information via Retrieval-Augmented Generation (RAG).** Geoptie’s GEO framework identifies "Answer Alignment" as the most consistent structural deficiency in underperforming content. Pages that open with keyword-rich company history lose the extraction race to content that provides clear, concise answers to target prompts immediately.

**Point 5: Audit heading structure for conversational format.**

**AI systems parse headings as signals for specific questions, requiring a shift from traditional keyword strings to conversational formats.** Reformat H2 and H3 tags as direct statements or questions that mirror buyer prompts in ChatGPT. This structural change ensures AI engines can accurately identify which specific user queries your content addresses.

| Traditional SEO Heading | AI-Optimized Conversational Heading |
| :--- | :--- |
| GEO Audit Best Practices 2026 | What are the best practices for a 2026 GEO audit? |
| GEO Audit Metrics | What does a GEO audit measure? |

**Point 6: Measure fact density.**

**Research from Princeton University (Aggarwal et al., 2023) demonstrates that adding authoritative quotes improves AI visibility by 41%.** The study, published on arXiv, confirms that including statistics and verifiable citations significantly boosts source visibility, while keyword stuffing reduces generative engine visibility by 10%. Audit your top 10 pages by counting data points, named citations, and specific statistics per 500 words against cited competitors.

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)

Content quality cannot compensate for infrastructure that AI crawlers cannot parse. This phase is the most technically demanding and the most commonly skipped step in a Generative Engine Optimization audit.

*   **Point 7: Implement an `llms.txt` file.** Proposed by Answer.AI co-founder Jeremy Howard, `llms.txt` is a Markdown file placed at your root directory that acts as a curated roadmap for AI crawlers. It filters out JavaScript noise, navigation elements, and DOM complexity, providing LLMs with a clean summary of canonical content. Platforms like Vercel, Anthropic, and Stripe use it to feed structured data to coding assistants and agents. Without this file, AI crawlers navigate without a map, often extracting incomplete or inaccurate brand information.

```markdown

# Mersel AI
> Generative Engine Optimization (GEO) Audit Framework

## Core Resources
- [GEO Audit Guide](https://www.mersel.ai/blog/geo-audit)
- [Technical Infrastructure](https://www.mersel.ai/generative-engine-optimization)
```

*   **Point 8: Audit schema markup completeness.** Schema.org markup serves as the metadata fuel for vector databases and RAG systems powering AI answers. Sites must audit for correct, error-free implementation of four essential schema types: `Organization`, `Product`, `FAQPage`, and `Article`. Missing `FAQPage` schema is particularly costly because FAQ content is a high-converting format for AI citation. Use Google's Rich Results Test and Schema Markup Validator to identify errors rather than just presence.

```json
{
  "@context": "https://schema.org",
  "@type": "Organization",
  "name": "Mersel AI",
  "url": "https://www.mersel.ai",
  "description": "Generative Engine Optimization (GEO) solutions."
}
```

*   **Point 9: Verify AI crawler access in `robots.txt`.** Many sites have legacy `robots.txt` configurations that inadvertently block AI-specific user agents. Explicitly check for GPTBot, ClaudeBot, and PerplexityBot to ensure visibility. If these bots are blocked, content and schema optimization will not impact AI engine results. Conversely, confirm that gated, proprietary, or legally sensitive content directories are explicitly protected from these crawlers.

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 to prevent audit decay as LLMs continuously update training sets and retrieval algorithms.** Citation patterns shift constantly, requiring a system that routes performance data directly back to content and technical teams. The audit remains incomplete until this feedback loop is established. Without this loop, organizations optimize based on assumptions rather than what works for their specific category.

| Platform | Action Item | Specific Requirement |
| :--- | :--- | :--- |
| **GA4** | Create Custom Segments | Isolate traffic from `chatgpt.com`, `perplexity.ai`, `claude.ai`, and other AI referrers. |
| **Google Search Console** | Track AI Overview Queries | Separately monitor impressions and clicks for AI-specific search results. |
| **Operational Cadence** | Monthly Review (Minimum) | Review content citations, prompts driving qualified inbound, and visibility shifts. |

Define a regular cadence to review which content earns citations and which prompts drive qualified inbound traffic. This data-driven approach ensures optimization is based on actual performance. The guide on [what metrics to track for AI search performance](/blog/what-metrics-should-i-track-for-ai-performance) provides detailed instructions on building this tracking setup in GA4 and GSC.

# Why This Sequence Is the Right Order

Establishing the baseline first ensures that content and infrastructure changes are based on prompts that matter to buyers. Content audits precede infrastructure because content gaps are faster and cheaper to fix, allowing infrastructure work to prioritize pages with existing citation potential. Measurement is the final step because meaningful change requires a baseline and initial fixes to be in place before performance can be accurately tracked.

Teams that reverse this sequence, such as starting with a schema markup sprint, frequently waste engineering time on pages that do not appear in any buyer prompts. Following this established order prevents resource misallocation and ensures technical efforts support high-impact content. The sequence moves from strategic mapping to content optimization, technical support, and finally, long-term measurement.

# When DIY GEO Audits Stall Out

The 10-point framework is actionable, but execution remains the primary barrier for most organizations. While SEO teams typically handle Points 1 through 3 (Prompt Mapping) without friction, Points 4 through 6 (Content Extractability) often fail due to stretched content editing bandwidth. Points 7 through 9 (Technical Infrastructure) require engineering involvement that is rarely prioritized in current sprint backlogs.

Point 10 requires a custom analytics build that most standard GA4 setups lack out of the box. AthenaHQ research, led by ex-Google Search and DeepMind engineers, identifies execution as the biggest gap in current GEO implementations. Companies often possess visibility data but lack the specialized team required to act upon it effectively. Strategy alone does not improve AI engine visibility.

Organizations frequently invest in monitoring platforms like Profound or AthenaHQ, yet detailed reports on share of voice gaps and missing prompts often sit unused in Slack channels. This occurs because teams lack the bandwidth, technical knowledge, or cross-functional alignment to implement fixes. The dashboard becomes an expensive artifact of a problem nobody is actively solving.

The execution gap stems from resource realities, as hiring experts in LLM citation mechanics takes three to six months. Briefing engineers on AI crawler infrastructure requires building shared context that content teams often cannot provide. Even if these are resolved, there is often no feedback loop connecting 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. This managed path ensures that the 10-point framework is fully implemented, moving beyond static reports to active visibility improvements.

## The Mersel AI Two-Layer Execution Model

Mersel AI operates at two distinct layers to maximize brand visibility in generative engines. On the content layer, the system builds a prompt map from buyer questions sourced from sales recordings, competitor citation patterns, and the existing AI landscape. This process delivers publish-ready blog posts directly to your CMS on a continuous cadence. These articles prioritize AI citation through direct answers, clear entity relationships, explicit product positioning, and bottom-of-funnel formats like comparison posts and use-case breakdowns.

The Mersel feedback loop separates the service from standalone content production by integrating real-time data from Google Search Console, GA4, and AI referral traffic. The system tracks citations across ChatGPT, Perplexity, and Gemini to update and refine existing posts based on performance. This data-driven approach ensures the system learns from actual citation patterns rather than assumptions to improve visibility.

The infrastructure layer focuses on technical configurations that AI crawlers require for effective indexing without altering existing design or SEO setups. Mersel deploys `llms.txt` files, schema markup (`FAQPage`, `HowTo`, `Product`, `Organization`), entity mapping, and internal linking structures. Human visitors experience no visual changes, while AI crawlers receive a structured, citation-ready version of the brand.

### Service Model Comparison

| Feature | Mersel AI | Self-Serve Platforms (Profound, AthenaHQ) |
| :--- | :--- | :--- |
| **Service Model** | Fully managed service | Self-serve dashboard |
| **Ideal User** | Marketing teams wanting execution handled | Teams needing real-time prompt monitoring |
| **Control Level** | Managed execution | Direct UI access and internal analyst control |

### Performance Results and Industry Benchmarks

Client results demonstrate significant growth in AI visibility and referral traffic across various industries. A Series A fintech startup increased AI visibility from 2.4% to 12.9% over 92 days, resulting in a 152% increase in non-branded citations and 20% of demo requests influenced by AI search. A DTC ecommerce brand reached 19.2% AI visibility in art shopping prompts in 63 days, with AI-driven referral traffic increasing by 58%.

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

Determine your brand's standing across the 10 audit points by requesting a [free AI content assessment](/contact). The Mersel team provides a baseline visibility analysis for your specific 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 done in-house.** The timeline is divided into specific phases:
*   **Baseline Visibility (Points 1-3):** Completed in a few days via manual testing across ChatGPT, Perplexity, Claude, and Gemini, or faster with monitoring tools.
*   **Content Extractability (Points 4-6):** Requires a review of high-priority pages against specific citation criteria.
*   **Infrastructure (Points 7-9):** Dependent on engineering availability for schema deployment and `llms.txt` configuration.
*   **Measurement Setup (Point 10):** Configured in GA4 and GSC within a few hours using specific segments.

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

Traditional SEO audits and GEO audits measure fundamentally different metrics and should be conducted as separate exercises, although a strong SEO foundation supports GEO performance. According to Princeton University research (Aggarwal et al., 2023), keyword stuffing—a standard concern in traditional SEO—actually reduces generative engine visibility by 10%.

| Audit Metric | Traditional SEO Audit | Generative Engine Optimization (GEO) Audit |
| :--- | :--- | :--- |
| **Core Focus** | Domain authority, backlink profiles, crawl errors, keyword density, and page speed | AI citation frequency, content extractability for RAG systems, schema markup completeness, AI crawler accessibility, and share of voice |
| **Keyword Impact** | Standard optimization concern | Keyword stuffing reduces visibility by 10% (Princeton University) |
| **Execution** | Foundational setup | Separate exercise focused on AI engine behavior |

**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, as a brand appearing frequently in Perplexity may be largely absent from ChatGPT for the same prompt. Organizations can use budget tools like Profound, AthenaHQ, and Scrunch to automate multi-platform tracking. Manual testing with 10 to 15 prompts per platform provides a usable baseline for audits conducted without dedicated automation 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 your content for generative answers.** Each AI company publishes specific user agent identifiers in their documentation for reference. Be deliberate by protecting directories containing proprietary, legally sensitive, or gated content while opening the rest of the site to crawlers. After updating the `robots.txt` file, validate the changes using each crawler's published user agent string in a testing tool to confirm access is restored.

**How quickly can I expect results after fixing GEO audit issues?**

**Initial visibility lifts typically appear within 2 to 8 weeks of implementing content and infrastructure changes, according to industry data from published GEO case studies.** Meaningful pipeline impact, measured as AI-referred demo requests or qualified leads, generally takes 60 to 90 days. Real-time analytics company Tinybird achieved a 3x share of voice increase and 370% growth in LLM-referred traffic within three months. Results compound over time as the feedback loop identifies which content formats earn citations for your specific category and buyer prompts.

# Sources

1. Gartner: Search Engine Volume Will Drop 25% by 2026
2. Seer Interactive / SerpClix: AI Overviews Organic CTR Drop 61%
3. Search Engine Land: Google AI Overviews Drive Drop in Organic and Paid CTR
4. Princeton University / arXiv: GEO Research (Aggarwal et al., 2023)
5. arXiv PDF: GEO Research
6. Geoptie: Generative Engine Optimization Framework
7. Yotpo: What is llms.txt?
8. GoVisible: The Role of Schema Markup in GEO
9. Otterly AI: GEO Audit 2.0
10. Scriptbee: 10-Step Framework for GEO

# Related Reading

- [Mersel AI Methodology: From Audit to Domination](https://mersel.ai/methodology)
- [How to Improve AI Search Visibility for My Brand](https://mersel.ai/visibility)
- [The Compounding Refresh Loop in AI Content](https://mersel.ai/refresh-loop)

# Related Posts

[GEO · Mar 18]

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

**Organic search traffic is declining due to AI search cannibalization, which requires diagnosing the real source of the decline and identifying the correct GEO solution.** You can learn how AI search cannibalization works to address instances where organic traffic is declining with no clear cause. [GEO · Mar 18](/blog/why-is-organic-search-traffic-declining-the-ai-effect)

## Why Is Organic Traffic Declining in 2026? AI Search & Recovery Plan

**Organic traffic is declining in 2026 because the presence of AI Overviews causes organic click-through rates to drop by 61% and reduces the overlap between top-10 search results and AI citations from 75% to 17%.** These metrics indicate a fundamental shift in search engine behavior, requiring brands to diagnose their specific decline and implement strategies to recover lost pipeline.

| Metric | Impact of AI Overviews |
| :--- | :--- |
| Organic Click-Through Rate (CTR) | 61% Drop |
| Top-10 → AI Citation Overlap | Collapsed from 75% to 17% |

](/blog/why-organic-traffic-declining-2026)[GEO · Mar 18

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

**Zero-click searches mean that 58.5% of Google searches now end without a click, requiring businesses to transform AI citations into their primary top-of-funnel lead source.** This 58.5% statistic represents a fundamental shift in how users interact with search engines. Understanding what these zero-click rates mean for your pipeline is essential for maintaining growth. [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

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## 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 performed in-house.** The baseline visibility phase can be completed in a few days, while the content and technical infrastructure phases depend on the volume of pages and engineering availability for schema and llms.txt deployment.

### How is a GEO audit different from a traditional SEO audit?
**A GEO audit focuses on AI citation frequency and content extractability for RAG systems, whereas traditional SEO focuses on domain authority and keyword density.** Research shows that standard SEO tactics like keyword stuffing can actually reduce generative engine visibility by 10%, necessitating a shift toward fact density and direct answer blocks.

### What is Generative Engine Optimization and how does it work?
**Generative Engine Optimization (GEO) is a structured diagnostic that measures how well a website is understood, trusted, and cited by AI answer engines.** It works by optimizing the content layer for Retrieval-Augmented Generation (RAG) and the technical layer for AI crawler access, ensuring brands appear in conversational AI responses.

### Why is structured data optimization important for AI-driven search results?
**Schema.org markup serves as the metadata fuel for the vector databases and RAG systems that power AI answers.** Specifically, FAQPage, Product, and Organization schema help AI models extract clean, structured data to provide accurate brand recommendations and citations.

### How do AI models select which brands to cite in search results?
**AI models prioritize content with high fact density, authoritative quotes, and direct answers that align with the user's prompt.** According to Princeton University research, including statistics and verifiable citations significantly boosts source visibility in generative responses.

### How does Mersel AI compare to Profound?
**Mersel AI is a fully managed GEO service that handles execution, whereas Profound is a self-serve monitoring dashboard.** While Profound provides visibility data and share of voice gaps, Mersel AI closes the "execution gap" by building prompt maps and delivering publish-ready, citation-optimized content directly to a client's CMS.

## Related Pages
- [How AI Search Algorithms Read and Rank Content](/blog/how-ai-search-algorithms-read-and-rank-content)
- [GEO for B2B SaaS: A Practical Playbook (2026)](/blog/geo-for-b2b-saas-playbook)
- [AI Share of Voice: How to Measure Your Brand in ChatGPT](/blog/how-to-measure-share-of-voice-in-chatgpt)
- [Why Your Brand Is Invisible to AI Search: Fix Guide](/ecommerce-invisible-to-ai)

## About Mersel AI
Mersel AI specializes in optimizing brand visibility and recommendations by AI search engines like ChatGPT, Gemini, and Claude. By focusing on AI-driven content optimization and strategic GEO (Generative Engine Optimization) practices, Mersel AI ensures brands are prominently cited and recommended in AI search results, driving growth and qualified leads.

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        "text": "**AI models prioritize content with high fact density, authoritative quotes, and direct answers that align with the user's prompt.** According to Princeton University research, including statistics and verifiable citations significantly boosts source visibility in generative responses."
      }
    },
    {
      "@type": "Question",
      "name": "How does Mersel AI compare to Profound?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "**Mersel AI is a fully managed GEO service that handles execution, whereas Profound is a self-serve monitoring dashboard.** While Profound provides visibility data and share of voice gaps, Mersel AI closes the \"execution gap\" by building prompt maps and delivering publish-ready, citation-optimized content directly to a client's CMS."
      }
    }
  ]
}
```

```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",
  "publisher": {
    "@type": "Organization",
    "name": "Mersel AI"
  }
}
```