Query fan-out for local SEO: how AI search splits one query into many (May 2026)

Query fan-out is the mechanism that turns a single user-typed query into many simultaneous retrievals. It is the foundational behavior of Google's AI Mode, ChatGPT Search, Claude's web search tool, Perplexity, and every modern retrieval-augmented generation (RAG) system you might be cited by. For local SEO it changes the unit of optimization: you are no longer competing for one query, you are competing across the eight to fifteen sub-queries that one query becomes inside the retrieval pipeline. This guide is the technical playbook for how fan-out works, why it matters specifically for local search, and exactly what to optimize so your business surfaces across every leg of the fan.

What query fan-out actually is

Traditional search engines treat a query as a single string and score documents against it directly. Fan-out flips this: the query is treated as a prompt to an internal LLM, which decomposes it into several focused sub-queries before any retrieval happens. The sub-queries are issued in parallel to a multi-path retrieval system, the result sets are combined, and a synthesis model produces the final answer or citation list.

Three components make the mechanism work:

• A decomposition LLM call. The query is passed to a fast model with a system prompt that instructs it to generate N sub-queries covering different facets of the user's intent. Google's AI Mode uses a Gemini variant for this; OpenAI uses a fast GPT family model; Anthropic uses a Claude Haiku-class model. The cost is small (typically tens of milliseconds and a few hundred tokens), so the system can afford to run it on every query that triggers AI behavior.
• Parallel multi-path retrieval. Each sub-query fans out across three retrieval paths simultaneously: lexical (classical inverted-index match, BM25 or close variants), semantic (vector similarity against a dense embedding of the sub-query), and entity (Knowledge Graph lookups for entities mentioned or implied in the sub-query). Each path returns its own ranked list of candidates.
• Aggregation and re-ranking. The per-sub-query result lists are combined using a rank fusion algorithm (Reciprocal Rank Fusion is the dominant choice; some systems use weighted variants). The combined list is then re-ranked by a quality scoring step that weights factors like entity quality, freshness, geo-proximity (for local queries), and behavioral signals. The top N candidates are passed to the synthesis model.

The mechanism has clear lineage in the information retrieval literature. Press et al. (2022) introduced Self-Ask, an explicit prompting strategy that asks the model to decompose compound questions before answering. Yao et al. (2022) generalised this in ReAct, the reasoning-and-acting pattern that interleaves thought, sub-query, and observation steps. Gao et al. (2022) showed in HyDE that generating a hypothetical answer and embedding that for retrieval beats embedding the question directly. Modern production fan-out systems are descendants of these techniques, hardened for latency, scale, and adversarial input.

Why query fan-out matters specifically for local SEO

Local queries carry more implicit dimensions per query than almost any other class of search. Compare:

The asymmetry is the central insight of this guide. A local query does not retrieve one ranked list; it triggers a parallel set of retrievals that each produce their own ranked list. Listings that appear in many of those lists, even at modest ranks, beat listings that win one list but appear nowhere else. The optimization target shifts from “rank for the user-typed keyword” to “appear across the full sub-query set the user's query triggers”.

The same listing changes that lift Map Pack rank by one or two positions lift AI Overview citation rate by 15 to 30 percentage points, because the changes (services breadth, attribute completeness, review content depth) widen sub-query coverage rather than just compete on one keyword.

The fan-out pipeline, end to end

A typical local query running through Google's AI Mode in May 2026 passes through this pipeline in approximately 600 to 1200ms total. The same broad shape applies to ChatGPT Search, Claude, Perplexity, and Copilot, with implementation differences in the synthesis step:

1. 1

   Query receipt and intent classification

   The query is parsed for local intent. A classifier decides whether to invoke the AI pipeline at all (some local queries still route to the classic Map Pack only). Geo-anchor is resolved (GPS on mobile, IP on desktop, explicit location modifier overrides).
2. 2

   Decomposition LLM call

   A fast model receives the original query plus context (location, conversation history if any, time-of-day, user language preferences) and generates 6 to 15 sub-queries covering different facets of intent. The number is model-tuned: AI Mode generates more sub-queries for complex queries, fewer for simple ones.
3. 3

   Parallel multi-path retrieval

   Each sub-query is issued simultaneously against three retrieval paths: lexical (inverted-index match with BM25 scoring), semantic (k-nearest-neighbor search against a dense vector index of business and content embeddings), and entity (Knowledge Graph and structured data lookups). Each path returns its top K candidates with scores.
4. 4

   Rank fusion

   The per-sub-query, per-path ranked lists are combined using Reciprocal Rank Fusion. Each candidate's RRF score is the sum across all lists of 1/(k+rank), with k typically set to 60. The result is a single combined list weighting both position and frequency-of-appearance across lists.
5. 5

   Quality re-ranking

   The combined list is re-ranked by a quality scoring model that weights entity quality, schema richness, freshness, geo-proximity, behavioral signals, and (for local) the full Map Pack ranking inputs. This is where listings with clean entity records get their disproportionate boost over listings with the same raw signals but messy entities.
6. 6

   Synthesis and citation

   The top candidates are passed to the synthesis LLM with their content excerpts. The model produces the user-visible answer, citing 1 to 3 businesses inline (for AI Overviews) or weaving them into a conversational reply (for AI Mode). The selection of which candidates to cite is influenced by answer-quality scoring, not just rank.

Worked example: “best italian restaurant for date night near me”

A query like this fans out across at least six distinct intent facets. The sub-query list below is an observed pattern from our AI Mode visibility tracking, normalised across multiple runs:

Look at the listings most likely to be cited in an AI Overview for this query. They are not the listings with the most keyword density for “italian restaurant date night”. They are the listings that:

• Are categorized correctly (Italian restaurant primary category, so the lexical and entity paths both fire).
• Have services or attributes that include reservations, outdoor seating, and romantic-relevant ticked attributes.
• Have review content mentioning “date night”, “anniversary”, “romantic”, “intimate”, “ambiance”, picked up by the semantic path.
• Have a clean Knowledge Graph entity record with consistent NAP, schema markup, and sameAs links, picked up by the entity path.
• Have on-site content (a single page is enough) that describes the venue's evening atmosphere, booking process, and group sizes accommodated, which the semantic and lexical paths both consume.

Worked example: “emergency plumber tonight”

The differentiator for urgency-led local queries is operational signals: opening hours marked as 24/7 or with extended hours, an “emergency service” attribute ticked, services that include “emergency call-out”, “burst pipe”, “boiler emergency”, and review content where customers mention urgent situations.

Worked example: “trusted dentist near me”

The retrieval triplet behind every sub-query

Each sub-query runs through three retrieval paths in parallel. The mechanics differ; the practical implication is that you optimize for three different things at once. The system rewards listings that win on more than one path:

How different AI systems do fan-out

Every major AI search system implements query fan-out, but the details differ. Knowing which system retrieves you, with which decomposition behavior, lets you instrument your optimization work usefully:

The local SEO implications

Translating fan-out mechanics into operational priorities reframes the entire local SEO playbook. The dominant change: optimization targets shift from depth on individual keywords to breadth across facets the decomposition LLM is likely to generate.

The field-by-field playbook

Concretely, here is what to do across each editable surface of your local web presence to maximize fan-out coverage:

Measuring fan-out visibility

Traditional rank tracking measures position for a single query string. Fan-out visibility is a different metric and needs different instrumentation:

Practical instrumentation methods:

• Direct query testing. For each priority query, run it through Google AI Mode, ChatGPT Search, Claude with web search, and Perplexity. Record whether you are cited, whether competitors are cited, and what the AI surfaces wrote about each. Monthly cadence is enough for most operators.
• Decomposition probing. Use Perplexity's Pro Search mode (which shows decomposed sub-queries) to observe how your target queries are being fanned out. The sub-query patterns generalise across systems reasonably well, even though the exact decomposition is model-specific.
• Coverage audits. For each priority query, list the likely 8 to 15 sub-queries. For each sub-query, check whether your listing has the relevant lexical hooks (in your services, attributes, description, on-site content), semantic density (in reviews, Q&A, on-site content), and entity confirmation (schema, sameAs). The cells where all three are present predict citation; the cells where one is missing predict absence.
• Map Pack tracking as a proxy. Until dedicated AI surface visibility tooling matures, geo-grid Map Pack tracking remains the strongest single predictor of AI Overview citation rate. Our Geo-Grid Rank Tracking feature is purpose-built for this layer.

The aggregate weight of each lever, in May 2026

Pulling the operational levers together with approximate weights based on our matched-pair before-and-after testing across customer portfolios since AI Mode rollout in early 2025:

Common mistakes

Where this is heading

Looking at the trajectory of fan-out implementations from early 2025 to May 2026:

• Sub-query counts are rising. Early AI Mode decomposition generated 4 to 8 sub-queries per complex query; observed counts are now 6 to 15 with a long tail. The retrieval systems are becoming more aggressive about breadth as latency budgets allow.
• Cross-session context is being added. AI Mode and ChatGPT Search are increasingly incorporating prior conversation context and user history into the decomposition step. A user who has previously asked about romantic restaurants will get differently-decomposed sub-queries for a follow-up query.
• Hybrid retrieval is generalising. The three-path retrieval (lexical, semantic, entity) is the norm in May 2026. The interesting research direction is fourth-path retrieval against multimodal indexes (image, video, audio) alongside the three text paths.
• Personalized fan-outs are emerging. Decomposition itself, not just retrieval, is becoming user-conditioned. The same query from two different users now fans out into partially-different sub-query sets.
• Latency budgets are tightening. AI Overviews have to render fast enough to feel responsive. The pressure on the decomposition step is to be cheap and predictable, which favours smaller, faster models with stable behavior.

The audit checklist

• List your top 10 commercial queries and the 8-15 sub-queries each is likely to fan out into
• For each sub-query, check whether your listing has lexical hooks (services, attributes, description fields)
• For each sub-query, check whether your reviews and Q&A mention the relevant concepts (semantic path)
• For each sub-query that names a regulator or credential, confirm it appears in your schema and sameAs
• Primary GBP category is the narrowest accurate option Google offers in your region
• Three to six secondary categories, each genuinely describing additional work you do
• Services list populated with 10 to 30 entries, each with a brief description
• Every applicable attribute ticked; reviewed at least quarterly
• Description uses the full 750 characters and leads with what you do
• 10 to 20 Q&A entries answering the top customer questions
• Reviews from the last quarter all have substantive replies
• Review content includes mentions of the specific services and occasions you want to be found for
• LocalBusiness schema markup on your site with consistent NAP and full sameAs
• Schema agrees with GBP on every fact; no contradictions
• Service pages (one per major service line) with genuinely differentiated content
• Location pages with genuinely distinct content per location, no programmatic templating
• Mobile-first page weight and core web vitals in green
• Monthly check of AI Overview citation rate across your top queries
• Monthly check of ChatGPT Search, Claude, and Perplexity for the same queries
• Geo-grid Map Pack tracking running as the leading indicator for AI surface visibility

Sources

Claims on this page are drawn from Google's public AI documentation, peer-reviewed information retrieval research, the documented web search behavior of each AI assistant, and our own observational testing across customer portfolios since AI Mode's wider rollout:

• Generative AI in Google Search (Google blog), the original public explainer of AI Overviews and the underlying decomposition behavior.
• AI Overviews and Search updates (Google blog), Google's running notes on AI Mode and AI Overview behavior.
• Measuring and Narrowing the Compositionality Gap in Language Models (Press et al., 2022), the Self-Ask paper that formalised explicit query decomposition.
• ReAct: Synergizing Reasoning and Acting in Language Models (Yao et al., 2022), the canonical paper on interleaved reasoning-and-acting that underlies most modern decomposition.
• Precise Zero-Shot Dense Retrieval without Relevance Labels (Gao et al., 2022), the HyDE paper showing hypothetical-answer embedding for semantic-path retrieval.
• Reciprocal Rank Fusion (Cormack, Clarke, and Büttcher, 2009), the canonical RRF paper defining the rank fusion algorithm used in most production multi-retrieval systems.
• Claude web search tool (Anthropic docs), the documented behavior of Claude's web search, including multi-step retrieval patterns.
• Schema.org LocalBusiness, the canonical schema definition for the entity-path layer.
• Our own internal correlation work: monthly aggregate observational analysis across customer portfolios in the UK, US, Canada, and Australia, against AI Overview, AI Mode, ChatGPT Search, Claude web search, and Perplexity visibility changes since early 2025.

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