Grounding queries and fan-out queries are the two AI retrieval mechanisms most SEO strategies miss entirely. Before a single word of an AI-generated answer is written, modern AI systems run at least two distinct retrieval operations beneath the surface. Understanding how each works is now central to any serious AI search visibility strategy and the fastest-growing discipline within generative engine optimisation (GEO). 

Most teams continue optimising for rankings they can measure in Google Search Console. However, the platforms that now shape purchasing decisions, brand perceptions, and research outcomes, including ChatGPT, Perplexity, Microsoft Copilot, and Google AI Mode, retrieve content through an entirely different logic. That logic has two layers. Brands that understand both gain a structural advantage that compounds over time. 

What is a Grounding Query in AI Search?  

A grounding query is a targeted retrieval an AI system runs before generating its answer. Built on the principles of Retrieval-Augmented Generation (RAG), these queries anchor the response in verifiable, current data. The goal is not to gather a broad range of sources, but to confirm the baseline truth of a single claim before the answer is composed. 

Think of grounding as the AI’s internal fact-check. If a user asks whether a particular software tool is still market-leading, a grounding query retrieves current market share figures, a recent industry survey, or an official changelog. Not because these are the most engaging sources, but because they establish a factual floor the response can stand on. 

What makes this strategically significant is that grounding sources are often uncited. As Hive Digital’s research into Bing AI Performance data shows, a page can receive over 1,000 AI grounding citations while registering near-zero impressions in traditional search results. The two visibility systems have become almost entirely separate. 

Brands whose data is retrieved for grounding earn an invisible form of authority. Their figures shape the AI’s confidence in a claim without any explicit attribution appearing in the final answer.  

Grounding Query: Key Characteristics 

• Narrow in scope: verifies one claim at a time 
• Executed against structured, authoritative, institutional sources 
• Results used as anchors, not primary content sources 
• Often triggers invisibly with no explicit citation in the final output 

What is a Fan-Out Query and Why Does it Matter for AI Visibility? 

A fan-out query is structurally the opposite of a grounding query. Rather than drilling down to verify a single fact, the fan-out process expands outward, spawning multiple parallel sub-queries to build a richer picture of a topic. SUSO Digital describes this as the AI doing “extra homework by checking multiple angles and sources” before formulating an answer. 

Fan-out queries are triggered by advisory, comparative, and exploratory questions. When a user asks “what should I consider when choosing a CRM for a growing SaaS business?”, a sophisticated AI system may simultaneously retrieve across four source types: 

1. Comparative reviews from aggregator platforms such as G2 and Capterra 
2. Expert opinion from analyst reports, practitioner blogs, and specialist newsletters 
3. Structural frameworks from official documentation, feature matrices, and pricing pages 
4. Social signals from forum discussions, community threads, and peer recommendations 

Each thread represents a distinct opportunity. As iPullRank’s research on fan-out architecture confirms, AI retrieval systems evaluate individual passages rather than entire pages. Granular, independently useful content performs better in fan-out retrieval than long-form articles built around a single keyword cluster.  

Grounding Queries vs Fan-Out Queries: Side-by-Side Comparison 

The practical implications for AI SEO strategy diverge sharply when you consider how each query type selects and weights sources. The table below compares the two retrieval layers across seven strategic dimensions. 

Dimension	Grounding Query	Fan-Out Query
Purpose	Verify a single claim or fact	Synthesise a landscape of information
Retrieval breadth	Narrow: one or two authoritative sources	Wide: multiple sources across content types
Source preference	Structured, verifiable, institutional	Diverse: expert, social, editorial, official
Citation behaviour	Often uncited; shapes framing invisibly	Partially cited; sources surface in response
Brand opportunity	Become the data source the AI trusts	Appear across multiple retrieval threads
Content that wins	Primary research, statistics, official data	Thought leadership, comparisons, expert takes
Optimisation lever	Specificity and authority of claims	Coverage breadth and source type diversity

Why Most AI Visibility Strategies Miss Both Retrieval Layers

Traditional SEO optimisation, even the modern flavour that accounts for Google AI Overviews and featured snippets, is calibrated to a third retrieval mode: the ranked-list query, where a user’s question maps onto a results page in order of relevance. 

Grounding queries and fan-out queries do not work like that. A grounding query is not asking for the best ten articles on a topic. It is asking for the most precise, most current, most citable data point on a specific claim. The document that wins is not the one with the highest domain authority. It is the one with the most authoritative statistic. 

Fan-out queries are looking for coverage across diverse source types. A brand with one excellent long-form article is less visible in a fan-out result than a brand with a medium-quality presence across a review platform, a practitioner forum, an analyst blog, and its own documentation. Surface area beats depth in fan-out retrieval. 

The Grounding Trap: Why Primary Research is Your Most Undervalued AI SEO Asset 

Grounding sources tend to be institutional and structured. They reward a specific type of content investment that few brand teams make: primary research that produces citable statistics. Not thought leadership. Not opinion. Genuine original data that an AI system can retrieve when it needs to confirm a claim. 

Brands that invest in annual surveys, proprietary benchmarks, and regularly updated data publications are building the content grounding queries select, often without realising it. Brands that do not are ceding that invisible authority to whoever does. 

The Fan-Out Opportunity: Why Distribution is Now a Core AI SEO Strategy

Because the AI is actively seeking diverse source types during fan-out retrieval, a brand whose presence is concentrated in one channel is structurally disadvantaged. A brand mentioned in a respected industry newsletter, cited in a forum thread, referenced in a comparison tool review, and documented in an analyst briefing will out-represent a brand with a single canonical deep-dive, even if that deep-dive is objectively better content. 

A Five-Step Diagnostic for AI Search Visibility: Grounding and Fan-Out Audit 

The practical starting point is an audit question most brands have not asked: for the queries your audience is submitting to AI systems, which retrieval mode are those queries triggering, and what does your content portfolio look like in that mode? 

1. Map query intent to retrieval type. Informational and statistical queries trigger grounding. Advisory and comparative queries trigger fan-out. Use Semrush or Ahrefs to cluster your target queries by intent type. 
2. Audit grounding candidacy. Identify whether your organisation publishes original survey data, regularly updated statistics, or institutional reports. The Bing Webmaster Tools AI Performance tab exposes grounding query data directly. 
3. Audit fan-out coverage. Map the content types an AI might retrieve in a fan-out query on your topics: aggregator reviews, forum mentions, analyst citations, editorial features, and official brand content. 
4. Identify the retrieval gap. The gap is where your content is least likely to be selected for the retrieval type that matters most for your audience’s queries. 
5. Build a two-track content strategy. For grounding: invest in original research and citable data. For fan-out: invest in presence diversity across earned media, forum participation, analyst programmes, and third-party review platforms. 

The Compounding Effect of AI Retrieval Presence 

AI systems that retrieve a source for one grounding query are more likely to weight it in future grounding retrievals on related topics. Brands that appear across multiple fan-out threads are more likely to be selected as the AI synthesises an increasingly broad landscape of adjacent questions. 

The window for establishing early positioning advantages is likely narrow. Organisations that build both the data infrastructure for grounding visibility and the distribution infrastructure for fan-out visibility now are establishing retrieval patterns that become progressively harder to displace. 

This is not primarily a content quality problem. Most organisations competing for AI search visibility already produce high-quality content. It is a content architecture problem: a question of whether the types of content being produced, and the channels through which they are distributed, match the retrieval logic of the AI systems through which their audiences now access information. 

At Adapt, we help brands build the content architecture that AI systems actually retrieve. Whether that means establishing grounding authority through original research or expanding fan-out coverage across trusted source types, our AI visibility audits identify exactly where you are invisible and what to do about it. Request an audit to understand your grounding and fan-out visibility across ChatGPT, Perplexity, and Google AI Mode. 

Frequently Asked Questions About Grounding Queries and Fan-Out Queries