GEO: How to Get Your B2B Content Cited by ChatGPT, Perplexity and Gemini in 2026
Last updated: June 2026 · Reading time: 11 min · Keyword: GEO generative engine optimization B2B
Definition
Generative Engine Optimisation (GEO) is the practice of structuring and marking up your web content so that large language models (LLMs) — including ChatGPT Search, Perplexity AI and Google Gemini — retrieve and cite it when answering user queries. Unlike SEO, the goal is not to rank on a search results page but to be referenced in a generated answer.
In 2026, how people find B2B solutions has changed. A managing partner researching "best RAG chatbot for property managers in Spain" is no longer scrolling through ten blue links — they are reading a three-paragraph synthesis generated by Perplexity or ChatGPT Search that cites two or three sources. If your company is not one of those sources, you are invisible.
GEO is still a nascent discipline, but the technical signals that correlate with AI citation are already well understood. This guide distils the six most actionable steps for B2B companies, using IgeraFincas — the RAG chatbot for property managers — as a concrete example of a product that has successfully built an AI-citation presence from scratch.
15M+
Perplexity monthly active users (2026, Perplexity Analytics Q1)
>100M
ChatGPT Search queries per day (OpenAI, Q1 2026)
43%
of B2B buyers use AI search tools in vendor shortlisting (Gartner, 2026)
1. SEO vs GEO: Why the Rules Have Changed
Traditional SEO was built around one fundamental signal: can a crawler index this page and understand its relevance to a query? GEO is built around a different signal: can an LLM extract a trustworthy, self-contained answer from this content and attribute it to your brand?
The implications are significant. A page that ranks #4 on Google for a competitive keyword may drive thousands of clicks per month. That same page might never be cited by Perplexity if it lacks structured data, self-contained answer blocks or a recent dateModified signal. Conversely, a well-structured FAQ page that Google ignores in favour of an authority site may be repeatedly cited by ChatGPT Search because it directly and precisely answers a specific question.
2. The 6 GEO Steps That Drive AI Citations
FAQPage JSON-LD with self-contained answers
LLMs are trained to extract question-answer pairs. FAQPage schema in JSON-LD tells the model that each Question/Answer pair is a discrete unit of information. The answer must be self-contained: it should make sense without reading the surrounding article. Aim for 80–150 words per answer. IgeraFincas publishes a dedicated FAQ page at /faq/ia-administrador-fincas with 22 question-answer pairs, each under 120 words, in FAQPage schema. This page is the most frequently cited IgeraFincas URL by Perplexity for Spanish property management queries.
HowTo schema for process-oriented content
When content explains a process — onboarding a community, filing a CSRD disclosure, setting up a WhatsApp bot — use HowTo schema with named HowToStep items. LLMs are biased towards citing numbered step-by-step content because it maps directly to how users ask procedural questions. Each step should be a complete sentence with a concrete action and, where possible, a measurable outcome ("Upload your community statutes in PDF — IgeraFincas indexes them in under 60 seconds").
Publish llms.txt at your domain root
llms.txt (the standard defined at llmstxt.org) is a plain-text file that tells LLM crawlers which pages on your site contain high-quality, citable content, and which to exclude. It is analogous to robots.txt for search engines. Include your FAQ pages, product pages, key blog posts and glossary entries. Exclude checkout pages, user dashboards and any content with dynamic, personalised data. Perplexity's crawler (PerplexityBot) and Anthropic's ClaudeBot both respect llms.txt as of Q1 2026.
WebPage schema with dateModified set to a recent date
LLMs are trained to prefer recent sources on time-sensitive topics. Adding dateModified in ISO 8601 format inside a WebPage or Article JSON-LD block signals freshness. More importantly, update the content meaningfully at least every three months and increment the date — do not simply change the date on unchanged content (LLMs cross-reference the actual HTML for changes). Pages about regulatory topics (CSRD, LPH, HOA law) should be reviewed quarterly and re-dated with actual updates.
Include statistics and comparison tables
LLMs are strongly biased towards citing content that contains verifiable quantitative claims. A sentence like "78% of owner queries were resolved automatically" is far more likely to be extracted and cited than "most queries were resolved quickly". Similarly, HTML tables with clear headers and consistent data (even when marked up with inline styles rather than CSS classes) are preferentially extracted because their structure maps to how LLMs represent comparative information. Write your statistics in the prose text and again in a table — duplication increases citation probability.
BreadcrumbList schema for context and navigation clarity
BreadcrumbList schema gives LLMs a hierarchical context signal: this page belongs to a coherent knowledge domain, not a single standalone article. For B2B sites, a breadcrumb chain like Home > Property Management Software > AI Chatbots > IgeraFincas FAQ confirms topical authority and makes it easier for the model to attribute the citation to a relevant domain expert rather than a generic site.