When someone asks Perplexity about your industry,
whose brand gets cited?
AI systems now answer questions directly — without sending users to a search results page. GEO structures your brand so ChatGPT, Perplexity, Gemini, and Google AI Overviews cite you when users ask questions you should own. These systems do not cite what sounds credible — they cite what they can verify, the same way any trusted source always has.
60-Day Measurable Progress Guarantee: if you don’t see measurable improvement in search visibility, AI citation frequency, or technical health within 60 days, I work an extra month at no charge.
Search has a second layer. Most businesses are invisible in it.
Google and Bing still matter. But a growing share of discovery now happens inside AI systems that generate direct answers — no list of links, no page visits. Users ask ChatGPT which consultant to hire. They ask Perplexity which agency is recommended for real estate leads in Dubai. The system answers from what it knows, cites sources it trusts, and the user acts on that answer.
If your brand is not structured for this layer, it does not appear in it. Generative Engine Optimization (GEO) is the practice of closing that gap.
Getting cited by AI systems that generate answers
ChatGPT, Perplexity, Gemini, and Claude pull from training data, live web access, and trusted sources. GEO optimises your entity signals, content structure, and llms.txt accessibility so these systems recognise your brand as citable authority in your niche.
AI systems cite differently than search engines rank
A page can rank #1 on Google and never appear in ChatGPT answers. The citation signals are different: entity consistency, structured data depth, topical authority breadth, and explicit machine-readable disclosure via llms.txt. SEO and GEO must be built in parallel.
What GEO implementation actually involves
GEO is not a single deliverable. It is a set of structured changes to how your brand is represented, how your content is organised, and how AI systems can read and verify your authority.
Entity audit and establishment
Map how AI systems currently understand your brand. Identify gaps in entity recognition — name, location, services, credentials, relationships. Build a consistent entity signal across your website, schema, and off-site references.
Schema and structured data implementation
Deploy the schema types AI systems use to verify authority: Person, Organisation, Service, FAQPage, HowTo, BreadcrumbList. Validate across Google Rich Results Test and AI-specific parsers.
llms.txt and AI-readable disclosure
Implement llms.txt — the emerging standard for communicating to AI systems what your site contains, what you want cited, and how to navigate your content. Currently adopted by Perplexity and a growing set of AI agents.
Topical authority and content architecture
AI systems cite sources that demonstrate deep, consistent expertise on a topic. Build the content surface area — question-based pages, definitional content, comparison content, case evidence — that establishes your brand as the citable authority in your niche.
Citation monitoring and iteration
Track how often and in what context AI systems cite your brand. Identify citation gaps and the content types that drive inclusion. Refine authority signals over time as AI training and retrieval patterns evolve.
Full GEO implementation, not a checklist
Every engagement covers the complete set of changes required to establish AI citation presence — not a subset of recommendations delivered as a report.
Baseline analysis of how ChatGPT, Perplexity, and Gemini currently represent your brand. Citation frequency, accuracy, and gaps documented.
Consistent entity representation across schema, NAP, about pages, and off-site references. Entity graph mapping for your niche.
Person, Organisation, Service, FAQPage, HowTo, BreadcrumbList schemas deployed and validated. Machine-readable authority layer.
AI-readable site disclosure file configured, structured, and submitted. Tells AI systems what to cite and how to navigate your content.
Topical authority map and content gap analysis. Priority pages identified for question-based, definitional, and comparison content.
Monthly reporting on AI citation frequency, sentiment, and competitive positioning. Iterative optimisation as AI systems evolve.
What people ask before starting
What is GEO Optimisation?
GEO (Generative Engine Optimisation) is the practice of structuring your brand, content, and entity signals so that AI systems like ChatGPT, Perplexity, Gemini, and Google AI Overviews cite you when users ask relevant questions. Unlike traditional SEO which targets ranked links, GEO targets cited answers in AI-generated responses.
How is GEO different from SEO?
SEO targets traditional search engines and ranked link results. GEO targets AI systems that generate direct answers — these systems cite sources differently than search engines rank them. A site can rank well in Google but never appear in ChatGPT or Perplexity answers. GEO addresses the AI citation layer specifically, while SEO addresses the ranked results layer. Both are required for full visibility.
How long does GEO take to show results?
AI citation patterns typically shift within 6 to 12 weeks of GEO implementation. The primary factors are entity establishment (structured data, consistent NAP, schema), content authority (cited sources, topical depth), and llms.txt accessibility. Some clients see citation improvements within 4 weeks on fast-moving AI systems like Perplexity.
Does GEO replace SEO?
No. GEO and SEO address different discovery layers. Traditional SEO remains essential for Google and Bing search traffic. GEO addresses the growing volume of users who get answers directly from AI systems without clicking a search result. The strongest position is visibility in both layers simultaneously.
Is GEO relevant for my industry?
If your buyers use ChatGPT, Perplexity, or Google AI Overviews to research services before purchasing, GEO is directly relevant. This includes real estate, professional services, healthcare, e-commerce, and most B2B sectors where AI-assisted research is common in the buying journey.
What is llms.txt and why does it matter for GEO?
llms.txt is an emerging convention for communicating directly with large language models about your site’s content, permissions, and preferred citation structure. Placed at your root domain, it tells AI crawlers what content to index, what to cite, and how to navigate your site structure. Perplexity actively reads llms.txt. Other AI systems are adopting it as the standard grows.
How do AI systems decide which sources to cite?
AI systems weight sources based on: entity recognition, topical depth, content freshness, schema markup, and cross-referencing from other trusted sources. GEO addresses all of these systematically rather than optimising for any single factor.
Can I measure whether GEO is working?
Yes. Citation tracking involves directly querying AI systems with the questions your buyers ask and recording when and how your brand appears in the response. Baseline is established before implementation. Monthly tracking compares citation frequency, citation accuracy, and query coverage across ChatGPT, Perplexity, Gemini, and Claude.
Find out if your brand is being cited —
or if a competitor is.
If your brand is not appearing in AI-generated answers for questions you should own, reach out. Direct access from the first message.