Why being cited by ChatGPT changes the B2B game
As of May 2026, ChatGPT has more than 800 million weekly active users (OpenAI confirmed figure on their official blog in April 2026). A growing share of these queries is commercial or comparative — "best ERP for an industrial SME", "GEO agency Paris", "Stripe vs Adyen". When your brand gets named in the answer, you bypass Google, you bypass ads, you land straight into the prospect's consideration set.
The GEO study by Pranjal Aggarwal et al. (Princeton CS, 2024, paper "GEO: Generative Engine Optimization") demonstrated that targeted content optimisation can raise a source's visibility inside generative engine answers by 30 to 40%. That's the order of magnitude NEXUS GEO reproduces across client engagements.
How ChatGPT actually picks its sources
ChatGPT draws from three distinct pools. The first is its training corpus, frozen at a given date (April 2024 for GPT-4o at release, more recent for GPT-5). The second is real-time browsing via Bing — sources must be indexed and considered authoritative by the engine. The third is the proprietary SearchGPT, which cross-references several indexes and favours content with high factual density.
To get cited, a brand must therefore be present in the right corpora, indexed by Bing, and structured so that passages are unambiguously "extractible". That's the exact opposite of pre-2020 SEO, where you tried to occupy the SERP. Here, you try to become a source the AI cites.
Step 1 — Citability audit (week 1)
Before any action, measure where you stand. NEXUS GEO audits 47 criteria grouped into four families: raw presence (Wikipedia, Wikidata, Crunchbase), technical citability (valid Schema.org, OpenGraph, llms.txt), editorial quality (factual density, H2/H3 structure, FAQ), and authority signals (editorial backlinks, press mentions, referring domain ratio).
The deliverable is a score out of 100 and a benchmark against three direct competitors on a panel of 30 industry prompts. Until you have that baseline, any action is blind. See our [full methodology](/methodologie) for the breakdown of the 47 criteria.
Step 2 — Schema.org: the non-negotiable technical layer
Schema.org is the vocabulary AIs use to understand your site without ambiguity. Without valid Schema.org Organization markup, ChatGPT has to guess who you are — and it can get it wrong, or worse, attribute your signals to a namesake. Google Search Central's 2025 documentation confirms that structured data is an eligibility signal for AI Overviews.
Priority types for B2B are Organization (identity), ProfessionalService or SoftwareApplication (offer), FAQPage (extractibility), Article (editorial content), Person (authors). Our [Schema.org for AI](/ressources/schema-org-pour-ia-guide-technique-2026) guide details the JSON-LD implementations line by line.
Step 3 — llms.txt: the AI welcome mat
The llms.txt standard, proposed by Answer.AI in September 2024, became dominant in less than twelve months. Anthropic, Vercel, Mintlify, Cloudflare and hundreds of tech brands have deployed it. It's a Markdown file placed at the site root that summarises your value proposition, canonical pages and identity signals — under 4,000 tokens, so any LLM can read it in one pass.
Our field feedback across 22 rollouts: the effect on citation rate is visible in 3 to 6 weeks, especially with Perplexity and Claude, which consult the file first. Details in [llms.txt: the complete guide](/ressources/llms-txt-guide-complet).
Step 4 — Citable content: 5 writing rules
- One claim, one source. Every figure, every claim must be attributed to a dated source. AIs cross-check sources and favour traceable content.
- Definitions in the first sentence. LLMs extract opening sentences to answer "what is X?" queries. Put the definition under an H2 and answer in fewer than 40 words right below.
- High factual density. One number, date or proper noun every 3 to 4 sentences. The ratio recommended by the Princeton GEO study is at least 2 statistics per 500 words.
- Scannable structure. H2 every 200-300 words, ordered lists, comparison tables. AIs read by HTML block.
- Explicit FAQ. A FAQ section with Schema.org FAQPage at the end of every commercial page. That's the format ChatGPT and Perplexity reuse most directly.
Step 5 — Domain authority: the slowest layer
Technical citability is a prerequisite; domain authority decides your frequency of appearance. LLMs weight sources with signals comparable to Google PageRank: number of referring domains, editorial quality of those domains, coherence of the Knowledge Graph (Wikipedia, Wikidata, Crunchbase, the SIRENE company registry for France).
Three high-impact actions: (1) open a clean Wikidata entry with sameAs links to LinkedIn, official site and Crunchbase; (2) aim for 3 to 5 mentions per quarter in dated sector media (TechCrunch, Sifted, The Information for tech); (3) publish proprietary studies that media will cite — the most durable lever.
4 measured case studies
Case 1 — French HR SaaS, 18 employees
Starting point: 2 citations across 30 tested industry prompts (ChatGPT, Claude, Gemini, Perplexity). After 90 days of NEXUS GEO engagement (audit + Schema.org + llms.txt + 6 pillar pieces + Wikidata entry): 17 citations across 30 prompts. GEO score moved from 31/100 to 72/100. Monthly AI traffic: 0 → 840 visitors with a 6.2% demo request rate.
Case 2 — DTC fashion brand (premium footwear)
Brand already established on classic SEO, weak on GEO. After 4 months: presence across 23 conversational shopping prompts out of 35 tested ("best leather sneakers made in Europe", "ethical footwear brands"). ChatGPT now cites the brand in 41% of those queries vs 6% at start.
Case 3 — Regional law firm (Lyon)
Strong local Google presence, invisible on ChatGPT. Targeted engagement on 12 prompts like "recommended employment lawyer Lyon". After 75 days: jump from 0 to 8 citations, first inbound client signed via Perplexity in week 11. Fast ROI thanks to the high unit value per case.
Case 4 — Climate deeptech B2B startup
Target: citations inside thematic analyses ("French startups decarbonising industry"). Approach centred on producing 3 proprietary studies picked up by Les Echos, La Tribune and Sifted, plus Wikidata entry and full llms.txt. After 120 days: the startup is cited by default in thematic queries on Claude and Perplexity, and appears in 60% of generated sector comparisons.
Step 6 — Measure to steer
You can't steer what you don't measure. NEXUS GEO operates a proprietary tracker that queries ChatGPT, Claude, Gemini and Perplexity monthly on 30 industry prompts per client, computing three metrics: citation rate (prompts citing the brand / total), average position (rank inside the answer), and AI share of voice (brand citations / total citations across all brands).
To go further, see our dedicated article on [measuring AI citations](/ressources/mesurer-citations-ia-outils-methodes-2026) which compares free tools (Google Search Console, Bing Webmaster Copilot tab), paid tools (Profound, AthenaHQ, Goodie AI) and DIY approaches.
FAQ
Sources
- Aggarwal P. et al. — "GEO: Generative Engine Optimization" — Princeton CS, 2024 (arxiv.org/abs/2311.09735).
- OpenAI — Official blog, ChatGPT usage stats, April 2026.
- Google Search Central — "Structured data and AI Overviews" documentation, 2025.
- Profound — "AI-referred traffic conversion benchmark" study, 2025 (tryprofound.com).
- Answer.AI — llms.txt standard proposal, September 2024 (llmstxt.org).
- Similarweb — "AI chatbot traffic insights", 2024-2025.
Want to move beyond theory and apply this to your own site? [The NEXUS GEO audit](/audit/new) delivers it in 10 days. No commitment.
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