Traditional search engines list ten blue links. Generative engines synthesize one recommendation. DEJAN structures your digital footprint, entity graph, and retrieval signals so ChatGPT, Google AI Overviews, Perplexity, and Claude select and cite your business.
Submit your website URL to check how generative search models currently represent and cite your brand.
Search has shifted from indexing keywords on result pages to synthesizing answers inside neural context windows.
| Dimension | Traditional SEO | Generative Engine Optimization (GEO) |
|---|---|---|
| Primary Surface | Ten-blue-link search engine result pages (SERPs) | Synthesized conversational answers in ChatGPT, Google AI Overviews, Perplexity, and Claude |
| Primary Objective | Page rank positions and human click-through rate (CTR) | Selection rate (SR), direct citation inclusion, and brand recommendation |
| Algorithmic Mechanism | Inverted index matching, crawl frequency, and PageRank link graphs | Dense vector embeddings, retrieval-augmented generation (RAG), and neural attention weights |
| Content Architecture | Long-form keyword-targeted articles designed for skimming | Modular, high-density factual units engineered for RAG chunk extraction |
| Authority Signals | Anchor-text backlinks and domain-level metrics | Cross-web entity consensus, verified datasets, and third-party forum authority (Reddit, Wikipedia, trade journals) |
| Measurement Framework | Keyword rankings and organic sessions | Share of Model, citation frequency across prompt variants, and answer sentiment tracking |
Our structured methodology aligns client web infrastructure with the retrieval mechanics of modern foundation models.
We test hundreds of multi-turn conversational queries across ChatGPT, Gemini, Perplexity, and Claude. We measure baseline citation share, identify competitor recommendations, and establish exact model sentiment around your brand.
Large language models reason in entities rather than keywords. We align Schema.org JSON-LD markup, Wikidata entries, and public knowledge registries so models associate your brand with your target commercial topics without ambiguity.
Generative models retrieve and read content in token chunks. We re-engineer your core pages with modular, self-contained factual data units, explicit definitions, and data tables that language models reliably extract and cite.
When language models ground their answers, they prioritize consensus across third-party sources. We engineer external presence across industry publications, verified reviews, Wikipedia, and community forums that feed live model retrieval.
We configure crawl access and server response patterns for GPTBot, ClaudeBot, PerplexityBot, and Google-Extended. Clean semantic HTML without script barriers ensures rapid ingestion during live grounding cycles.
DEJAN developed Selection Rate Optimization to model how AI engines choose between multiple retrieved snippets. By optimizing snippet information density, we increase the probability that your content becomes the chosen citation source.
DEJAN builds GEO strategies on empirical machine learning research rather than superficial prompt tips.
DEJAN founder Dan Petrovic trained an autoregressive language model from scratch to study internal weight activations and representation formation. We use mechanistic probing to measure what a model believes about your category before applying targeted content interventions to steer its outputs.
Research published and shared via arXiv, ORCID, and academic citations.
In our 4-month enterprise trial with sports apparel manufacturer OWAYO, DEJAN tracked 6,776 high-intent AI queries across Google AI Overviews and ChatGPT. Brand recommendation share increased from 1.5% to 8.0%, establishing category dominance over established legacy competitors.
Read Full Case Study →Submit your domain to begin an evaluation of your brand's citation frequency and entity clarity across major AI platforms.
Straightforward answers regarding Generative Engine Optimization, timelines, and commercial performance.
Generative Engine Optimization (GEO) is the technical discipline of structuring web assets, entity relationships, and third-party citations so large language models select and cite your business as a direct recommendation in conversational answers.
Traditional SEO optimizes web pages for keyword positions on ten-blue-link search result pages. GEO optimizes content for retrieval-augmented generation (RAG) and model attention, maximizing the probability that conversational engines synthesize answers from your data.
DEJAN optimizes for Google AI Overviews, Gemini, OpenAI ChatGPT, Anthropic Claude, Perplexity, and Microsoft Copilot.
No agency can guarantee inclusion inside probabilistic neural networks. DEJAN uses mechanistic probing and mathematical optimization to align digital assets with model retrieval criteria, maximizing citation probability.
On-site structured data fixes, content extraction updates, and crawl directives reflect in live AI answers within two to four weeks. Off-site entity building and broad consensus realignment typically compound over three to six months.
Yes. Generative engines ground their answers in retrieved web pages. Fast crawlability, indexation, and technical site health remain the prerequisite foundation for AI retrieval.
Schedule a confidential strategy session with Dan Petrovic and our senior Australian search strategists.
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