How to Find Your AI Visibility Gap (Competitor Analysis)

Identifying an AI visibility gap is the process of uncovering the distance between the topics AI models associate with your competitors and those they associate with your brand. To close this gap, you must break down how Large Language Models (LLMs) retrieve, cite, and recommend information about your industry rivals.

Closing the AI Visibility Gap: A Strategic Framework

Figure out the AI Mention Gap

An AI mention occurs when a LLM names a brand in its response with or without providing a link, often during synthesized product recommendations. In the prompts where your rivals are surfaced as authorities but your brand is absent, that’s an AI mention gap.

In practice, identifying this gap requires examining not just individual prompts or queries, but the broader conditions under which competitors are surfaced across sources, platforms, and conversational contexts.

• Share of Voice Analysis: AI Visibility tools like Operyn allow you to generate a list of prompts to measure how often competitors appear relative to your brand across the same set of AI-generated responses.
• Third-Party Reliance: AI models derive their understanding of a brand primarily from third-party websites, such as industry reviews, news articles, and forums – a pattern we examined in our earlier analysis showing that mentions on highly-linked pages correlate most strongly with AI visibility. If a competitor appears more often, it is frequently due to a higher frequency of branded web mentions across these high-authority domains.
• Assistant Variance: Because the majority of sources cited by AI assistants are unique to each platform – for example, ChatGPT favors media partnerships while Google AI Overviews favors user-generated content, you must audit mention gaps separately.

Uncover the AI Citation Gap

An AI citation occurs when a model attributes specific facts or data to a domain and includes a direct link to it. If your rivals get more citations, they’re getting more credit for expertise. Uncovering a citation gap involves understanding both which competitor pages AI systems choose to cite and why those pages are selected over others.

• Cited Pages Audit: Use AI Visibility tools to monitor the cited pages reports for you and your competitors. Keep an eye to identify the exact content formats that AI models prefer to use as sources in your niche and follow the pattern. Across analyzed AI citations, we at Operyn consistently observe that data studies, “how-to” guides, and “X vs Y” comparisons are among the most frequently cited content formats within AI-generated responses.
• The Freshness Advantage: AI assistants show a significant preference for newer information. According to a research analyzing 17 million citations across 7 LLMs, content cited in AI answers is 25.7% fresher than traditional organic search results. A citation gap often exists simply because a competitor has updated their core content more recently than your brand.

Perform a Competitive Entity Gap Analysis

AI models understand the web through entities (brands, products, people) and the statistical relationships between them. An entity gap exists when the model perceives a competitor as the default authority for a sub-topic but does not recognize your brand in that context. In practice, diagnosing an entity gap requires probing how AI systems associate specific attributes and expertise with competing brands under controlled conditions.

As an easily replicated experiment, try to conduct a Temperature Zero Test: set Model Temperature to 0.0 to eliminate randomness from the output and force the model to select the most statistically probable completions for a competitor’s expertise versus yours. Compare prompts like “[Competitor] is known for” versus “[Your Brand] is known for”. If a model returns specific, technical associations for a rival (e.g., “lightweight,” “innovative”) but generic fluff for you, your brand lacks topical authority in the model’s training data.

Map the Competitive Prompt Gap

Beyond your own data, you can choose a third-party tool to discover the conversational paths AI uses to find your competitors. The goal is to identify which specific sub-queries and page structures AI systems rely on when validating competitor expertise.

• Competitor Keyword Fan-Out: AI assistants break down complex user prompts into simpler “fan-out queries” to retrieve information. By exporting the keywords your competitors rank for that trigger AI Overviews, you can see the specific sub-topics the AI model is using to validate their authority.
• Structural Gap Identification: If competitors are cited for high-intent queries that you also rank for in traditional search, you have a structural gap. This indicates that while your page is authoritative, the AI’s extraction layer found the competitor’s content easier to “chunk” and summarize, likely due to their use of Bottom Line Up Front (BLUF) formatting or clearer heading hierarchies.

Tactical Steps to Close AI Visibility Gaps

Once the specific mention, citation, entity, and structural gaps are identified, the next step is to apply targeted actions that directly address how and where AI systems source, extract, and rerank information.

1. Prioritize Off-Site Placements: Target the highly-linked or high-traffic third-party pages that already mention your competitors to secure your own placements.
2. Optimize for Extraction: Implement BLUF formatting within the first 30 passages of your most important pages to ensure AI models can retrieve your answers within their technical processing limits.
3. Diversify Platform Presence: Engage on YouTube and Reddit, as these are the second and third most cited domains in Google’s AI ecosystem.
4. Monitor Reranking Layers: Track your visibility across multiple models simultaneously, as each model uses unique ranking factors and different sets of preferred domains.

AI visibility gaps are measurable, not abstract. They stem from how competitors are mentioned, cited, and structurally surfaced across AI systems. By identifying these gaps and aligning content, off-site signals, and structure with how models retrieve information, brands can move from reactive optimization to deliberate, repeatable visibility in AI-generated answers.