The work that integrates classical SEO and Generative Engine Optimization end-to-end: technical and semantic optimization along the inference paths of Google AI Overviews, ChatGPT, Perplexity and Copilot. Passage-level citability, entity consolidation, retrieval scoring, schema architecture — orchestrated as one operating system.

“Ranking is a side-effect. Citation is the new conversion event.”
Classical SEO and GEO are not two separate disciplines — they share infrastructure, entities and content. The flagship engagement orchestrates four layers as one system.
Restructure content so that LLM models extract and cite single passages — clear claim, evidence, source, attribution. Style guide for AI-citable writing, passage scoring, evidence chains.
One canonical entity per brand, person and product. Wikidata anchoring, Schema.org @id graph, sameAs cluster, knowledge-panel readiness across DE, EN and additional locales.
Score and steer how content surfaces in retrieval-augmented generation: embedding density, semantic chunking, anchor diversity, query-intent coverage. Concrete signals, not vibes.
JSON-LD as a connected @id graph rather than fragmented snippets — Person, Organization, Article, Product, FAQ, BreadcrumbList. CI validation, deployment pipeline, governance.
Cross-model prompt audit across 1,000-2,000 curated prompts, six engines (GPT, Claude, Gemini, Perplexity, Copilot, AIO). Citation rate, share of model, sentiment drift as baseline KPIs.
Wikidata consolidation, Schema @id graph, sameAs cluster, author-entity build-out. Knowledge-panel readiness check across primary markets.
Restructure existing content for passage-level citability: claim-evidence-source pattern, citable opening sentences, glossary anchors, FAQ alignment.
Embedding density audit, semantic chunking review, anchor diversity, query-intent coverage scoring. Implementation roadmap for retrieval-friendly structures.
Weekly prompt-matrix tracking, monthly board-level reports, citation drift alerts, competitive citation share. Continuous adjustment.
Citation rate compounds as entity coherence stabilizes across model updates and training cycles. Outcome: durable citation share in primary AI surfaces.
4-6 weeks. Cross-model baseline, entity gap, schema review, prioritized roadmap.
All six phases. Entity, passages, retrieval, schema, monitoring — one integrated track.
Ongoing monitoring, monthly reports, content steering, schema governance, citation defense.
30-minute scope call: we map your current citation share and identify the highest-impact lever.