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.
Density alone isn't enough. Four structural signals make the difference in citation weighting.
01
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
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
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 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 guarantee
3,000+ word hub article
Strict structure: H2-per-facet covering 12 to 16 sub-questions, direct answer at the top of each section, deeper development next.
Fan-out mapping included
Map of target sub-questions built upfront via Slashr brief-contenu, validated with you before writing.
Published on a media in our network
Choice of a media with strong topical authority on the vertical to enable rapid ranking on the sub-queries.
Google indexing guaranteed
Search Console submission, indexation check, URL delivered with the order.
Advanced schema
FAQPage + Article + Organization with sameAs to your social profiles. Explicit dating (datePublished + dateModified).
Contextual mention of your brand
One or two mentions in the relevant section, as a source of expertise — not as the subject of the article.
What we don't promise
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.
Our spec
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.
Our spec
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.
Our spec
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.
Our spec
Advanced schema included
Article + FAQPage + Organization with sameAs are systematically marked up. We integrate your entity identity (Wikipedia if present, LinkedIn, X, Crunchbase profiles).
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.
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.
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.
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.
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.
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.
We map the fan-out of your target intent, identify the media in our network that fits, write and publish. Google indexing guaranteed.