Definition

What is Generative Engine Optimization?

Generative Engine Optimization (GEO) is the discipline of structuring content, entities and schema so that a brand is cited as a source in the answers of generative search systems: Google AI Overviews, Google AI Mode, ChatGPT, Perplexity, Microsoft Copilot, Gemini and Claude. Classic SEO optimizes documents for rankings; GEO optimizes the answer layer. That means self-contained, citable passages, one consistent entity per brand, person and product, and a connected JSON-LD graph. Success is measured with citation rate, AI answer rate (AAR) and share of voice in LLM answers, not with positions alone. Classic SEO remains the necessary foundation, a point Google itself makes: pages still need to be crawlable, indexable and genuinely helpful. GEO is the additional layer for generative visibility. In the flagship engagement I run both as one programme, from a cross-model baseline to continuous monitoring.

Related reading: GEO in the glossary, GEO vs. SEO and the GEO audit as a fixed-scope entry point.

01 — Four optimization layers

One integrated operating system.

Classical SEO and GEO are not two separate disciplines — they share infrastructure, entities and content. The flagship engagement orchestrates four layers as one system.

Passage-level citability

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.

CitabilityPassagesEvidence

Entity consolidation

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.

Wikidata@id graphsameAs

Retrieval scoring

Score and steer how content surfaces in retrieval-augmented generation: embedding density, semantic chunking, anchor diversity, query-intent coverage. Concrete signals, not vibes.

RAGEmbeddingsChunking

Schema architecture

JSON-LD as a connected @id graph rather than fragmented snippets — Person, Organization, Article, Product, FAQ, BreadcrumbList. CI validation, deployment pipeline, governance.

JSON-LDCI validationGovernance

02 — Six phases

From baseline to continuous citation growth.

Baseline (week 1-4)

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.

Entity work (week 4-8)

Wikidata consolidation, Schema @id graph, sameAs cluster, author-entity build-out. Knowledge-panel readiness check across primary markets.

Passage refactor (month 2-3)

Restructure existing content for passage-level citability: claim-evidence-source pattern, citable opening sentences, glossary anchors, FAQ alignment.

Retrieval scoring (month 3-4)

Embedding density audit, semantic chunking review, anchor diversity, query-intent coverage scoring. Implementation roadmap for retrieval-friendly structures.

Live monitoring (month 4+)

Weekly prompt-matrix tracking, monthly board-level reports, citation drift alerts, competitive citation share. Continuous adjustment.

Compounding (month 6+)

Citation rate compounds as entity coherence stabilizes across model updates and training cycles. Outcome: durable citation share in primary AI surfaces.

03 — Engagement models

Three shapes.

AUDIT

GEO Diagnostic

4-6 weeks. Cross-model baseline, entity gap, schema review, prioritized roadmap.

PROGRAMME

6-month flagship

All six phases. Entity, passages, retrieval, schema, monitoring — one integrated track.

RETAINER

Continuous

Ongoing monitoring, monthly reports, content steering, schema governance, citation defense.

SEO & GEOfor generativesearch systems.