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Format · Query fan-out pages

One single page for all 16 sub-searches of AI Mode

A 3,000+ word hub article that covers the entire fan-out of a single intent. Published on a media with strong topical authority in our network, Google indexing guaranteed.

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✓ 12-16 sub-questions mapped✓ 3,000+ words, strict structure✓ Google indexing guaranteed
Sample fan-out · "netlinking agency lyon"
  • Average prices in the region
  • Standard delivery time
  • Contractual guarantees
  • Verticals covered
  • Pricing model (package, per-unit)
  • How they differ from a marketplace
  • Local case studies
  • Ideal client profile
  • Validation process
  • Expected monthly reporting
  • Recommended anchor mix
  • +4 secondary sub-questions

12 to 16 H2 sections inside the same hub page.

Four criteria that decide

What makes a hub page citable by AI Mode

Density alone isn't enough. Four structural signals make the difference in citation weighting.

01

Container thinking: one page serves N sub-questions

The classic hub-and-spoke model (one page per sub-topic, all linked) is inverted for fan-out. Google AI Mode breaks every query into up to sixteen parallel sub-searches. The page that surfaces is the one that covers all the facets internally, not the one that scatters them across ten linked URLs. A single dense page, structured by sections, beats ten thin pages.

02

H2 per facet + direct answer at the top of each section

Answer engines look for a short, self-contained formulation they can drop into their summary. Each H2 should be a fan-out question, and the first paragraph should answer it in two or three sentences. The deeper development comes next for Google and the human reader. This dual structure serves both audiences without compromise.

03

Fan-out mapping via Slashr brief-contenu

We identify the twelve to sixteen implicit sub-questions of an intent through several sources: the Slashr brief-contenu API (which we operate), AlsoAsked, Keyword Insights, and direct observation of AI Overviews on the head query. Exhaustive fan-out coverage drives how AI Mode and Perplexity weight citation.

04

FAQPage + Article schema + dated sourcing

FAQPage encodes each Q&A as a structured object the LLM can isolate. Article + datePublished + dateModified anchor the freshness. Paragraphs cite dated sources (study, institution, methodology) because modern models penalize round, unsourced numbers.

What we deliver

The exact spec of the hub page we publish

What we guarantee
  • ✓ 3,000+ word hub articleStrict structure: H2-per-facet covering 12 to 16 sub-questions, direct answer at the top of each section, deeper development next.
  • ✓ Fan-out mapping includedMap of target sub-questions built upfront via Slashr brief-contenu, validated with you before writing.
  • ✓ Published on a media in our networkChoice of a media with strong topical authority on the vertical to enable rapid ranking on the sub-queries.
  • ✓ Google indexing guaranteedSearch Console submission, indexation check, URL delivered with the order.
  • ✓ Advanced schemaFAQPage + Article + Organization with sameAs to your social profiles. Explicit dating (datePublished + dateModified).
  • ✓ Contextual mention of your brandOne or two mentions in the relevant section, as a source of expertise, not as the subject of the article.
What we don't promise
  • × No guarantee of Google rank or AI citation. Fan-out density maximizes the odds, with no contractual commitment.
  • × No monthly monitoring of positions or citations. The page is delivered live; post-publication evolution is out of scope.
  • × No fan-out audit billed separately. Mapping is included in the article delivery, not sold as a standalone service.
  • × No annual update included. A future revisit is a new order.
Pitfalls to avoid

Four common mistakes on hub pages

How our writing spec sidesteps the most frequent traps.

⚠ Risk
Thin content built around a single keyword

A 1,500-word page built around one head keyword is ignored by AI Mode as soon as the fan-out covers angles the page doesn't address. The engine prefers a broader competitor, even one ranking lower organically.

✓ Exhaustive fan-out coverage

We systematically map the twelve to sixteen sub-questions and treat them all in the same page. That forces a 3,000+ word minimum and a strict section-by-section structure.

⚠ Risk
Brand too visible, perceived as disguised placement

If your brand appears ten times throughout the article, the engine codes it as commercial content and citation weighting drops. The LLM is looking for editorial sources, not glossy brochures.

✓ Contextual mention, in the relevant section

Your brand appears once or twice, in the section where it brings legitimate expertise, as a source. The rest stays editorial.

⚠ Risk
No sourcing, round numbers without dates

Modern LLMs aggressively filter content that throws out figures with no source. A paragraph saying « 95% of companies » with no citation is coded as low-trust marketing.

✓ Rigorous sourcing, dates and methodology

Every figure references a study, date, and methodology where relevant. The rigor of the sourcing is itself a citation signal.

⚠ Risk
Schema missing or sloppy

Without FAQPage, without properly marked-up Article, without sameAs to your social profiles, the engine struggles to reconstruct your brand identity and the page structure.

✓ Advanced schema included

Article + FAQPage + Organization with sameAs are systematically marked up. We integrate your entity identity (Wikipedia if present, LinkedIn, X, Crunchbase profiles).

The other GEO formats
"Best X" listicles

"Best X 2026" article with your brand placed transparently on top.
Brand mentions

Editorial article where your brand is cited as a source of expertise.

Frequently asked questions about query fan-out pages

Why 3,000+ words instead of a standard 1,500-word article?

Because Google AI Mode's fan-out breaks a query into up to sixteen parallel sub-searches. A 1,500-word article reasonably covers four to six sub-questions; you need two to three times more for the page to surface across the whole fan-out. The density isn't filler, it's what makes the page eligible for several sub-searches at once.

Is it published on my site or on your network?

On our network. That's our job: we're the editors and owners of the media. The topical authority of a network media lets the page rank quickly on sub-queries, whereas a young brand site would take six to twelve months to reach the same position.

How do you make sure the mapped fan-out is the right one?

We cross-reference several sources: the Slashr brief-contenu API (fan-out extraction from SERPs), AlsoAsked for People Also Ask, Keyword Insights for autocomplete, and direct observation of AI Overviews on the head query. We validate the final list with you before writing.

Do you guarantee Google rank?

No. Rank depends on factors outside our control (SERP competition, Google updates, etc.). What we guarantee is publication, the enforced format, full fan-out coverage, and Google indexing. The performance that follows is the consequence of a well-built article on a media with topical authority, not a contractual deliverable.

Can the article also rank on classic Google?

Yes, and it's a useful side effect. The H2-per-facet + direct answer + deeper development structure serves Google AI Mode just as well as featured snippets and classic positions. A well-built hub captures both AI traffic and SEO traffic in parallel, with no structural compromise.

How long until indexation and rank?

Indexation happens within a few days of publication via Search Console. For rank and AI citation, it varies: four to eight weeks on a vertical where the media already has recognized topical authority, three to six months on emerging verticals. No contractual commitment on these timeframes.

No middleman, no surprises

We map the fan-out of your target intent, identify the media in our network that fits, write and publish. Google indexing guaranteed.

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