From SEO to GEO: getting cited, not just clicked
The generative AI revolution has changed the destination for organic traffic. More and more users are asking questions in natural language to Google AI Overviews, Perplexity, ChatGPT Search, Claude, and Gemini, and receiving synthetic answers that cite (or don't cite) your brand. Classic SEO gets you into the ten blue links; GEO – Generative Engine Optimization – gets you inside the answer.
We support SMEs, companies, and agencies that want to stay visible in both worlds. We keep SEO best practices intact (structure, authority, performance, intent) and add the GEO layer: quotable content, clear entities, granular schema.org, proprietary data, recognizable authors, llms.txt, and AI bot governance. We work to ensure your site is read by both Googlebot and LLM crawlers, and that your content is chosen as an authoritative source.
How we optimize for generative engines
Our work focuses on three operational pillars that together measurably increase the probability of being cited in AI answers, while maintaining visibility on traditional search engines.
1
Optimization for AI Overviews and fan-out queries
Generative engines don't query a single question: they expand it into dozens of sub-questions (fan-out queries) to build an answer. We map your audience's real questions, rewrite content into self-contained paragraphs and Q&As, build interconnected thematic clusters, and produce passages designed to be extracted by Google AI Overviews, Perplexity, ChatGPT Search, Claude, and Gemini as direct answers.
2
Quotability, first-party data, and citations in AI engines
LLMs cite sources that have something to say. We work on the quotability of your content: clear definitions, original statistics and data, reusable key phrases as sound bites, concrete examples, and verifiable numbers. We build author authority (bios, LinkedIn, expertise) and govern the E-E-A-T signals that LLMs use to choose who to cite. Goal: appear in AI responses with your name, not just your URL.
3
Technical architecture for LLMs: schema.org, llms.txt, and AI bots
Structured data is the most direct way to talk to LLMs. We implement granular schema.org (Article, FAQ, HowTo, Product, Review, Breadcrumb, Person, Organization) and handle the technical GEO layer: llms.txt files to declare quotable content, AI bot management in robots.txt, performance for LLM crawlers, canonicalization, and entity disambiguation. Everything you need to be indexed, understood, and cited accurately.
