GEO: How to Optimize for ChatGPT, Claude & Perplexity

AEO gets you cited. GEO makes you the source AI engines preferentially choose. Here is how brand-entity signals, authority cues and citable data make you the default reference.

If AEO is "am I cited?", GEO is "am I the preferred source?". As AI search matures, the brands that own preferred-source status capture disproportionate value — repeated citations across queries, stronger brand-entity signals, and a defensible moat competitors can't replicate quickly.

What GEO actually is

Generative Engine Optimization (GEO) is the practice of building the authority, entity, and freshness signals that cause an LLM to pick your content over an equally-correct competitor's content. AEO is the format layer; GEO is the trust layer.

LLMs don't cite sources randomly. They weigh:

Entity recognition — does the model know your brand as a real, recognisable entity?
Authority signals — bylines, credentials, citations from trusted publications
Unique data — original statistics, research, surveys
Recency — when was this last updated?
Co-citation — who else cites you, and does the model trust them?

Why LLMs cite some sites and not others

Each major model has slightly different selection logic, but the patterns converge:

ChatGPT (Bing-powered search)

Bing's ranking signals + freshness + structured data. Pages with strong Bing rankings, recent timestamps, and FAQPage / Article schema get picked first.

Perplexity

The most aggressive about source citation (always shows them). Weighs domain authority + topical relevance + content depth. Long-form, well-cited content does best.

Claude (Anthropic)

Claude is more reluctant to cite specific brands but does pull from its training data plus optional web search. E-E-A-T signals, age of domain, and Wikipedia/Wikidata presence carry weight.

Google AI Overviews (Gemini)

Uses Google's ranking signals + a layer that prefers content with explicit definitional structure, schema, and AI-Overviews-friendly formatting.

The seven pillars of GEO

1. Brand-as-entity

The strongest GEO signal is the AI engine recognising your brand as a discrete entity. Build this by:

Creating/updating your Wikidata entry
Consistent NAP (Name/Address/Phone) across the web
Schema.org Organization markup with sameAs links to your social profiles
Profiles on Crunchbase, LinkedIn, industry directories

2. Original, citable data

LLMs strongly prefer content with quotable statistics. If you publish a survey, original research, or unique customer numbers — the model will lift them and cite you. Generic content gets paraphrased without attribution; original data gets quoted.

3. E-E-A-T proofs

Experience: "We've handled X cases since Y". Expertise: author credentials. Authoritativeness: media mentions, awards, certifications. Trust: HTTPS, privacy policy, real contact info, customer reviews. All four are part of the LLM's implicit ranking.

4. Recency signals

Add an explicit "Last updated" timestamp on every evergreen page. Update content meaningfully every 6-12 months. LLMs prefer recent content for time-sensitive queries.

5. Quotable single-sentence facts

Engineer 5-8 "quotable units" per page — single sentences that stand alone as facts. Format them clearly (bold, callout boxes). These are the lifts AI engines extract verbatim.

6. Co-citation building

Being cited alongside trusted sources improves your own trust signal. Get featured in industry round-ups, expert quotes, partnership announcements. The model learns "this brand appears with these other trusted brands → it's probably trusted too".

7. Schema for entity recognition

Beyond FAQPage and Article, use:

Organization with full sameAs links
Person for author bylines with credentials
Product / Service for specific offerings
Review + AggregateRating where genuine reviews exist

Case study patterns we see

Across our GEO engagements, three patterns consistently move citation count:

Adding 5+ unique statistics per page — increases citation rate by 60-90%.
Deploying author bylines with credentials — increases Claude/Perplexity citation by ~40%.
Updating Wikidata entry (where applicable) — improves cross-engine recognition by 2-3×.

How to measure GEO success

Like AEO, traditional rank tracking misses GEO. Track:

Mention count per AI engine, per target topic, weekly
Source rank — when cited, are you first, third, or fifth?
Share-of-voice vs your top 3 competitors on key topics
Brand-query response quality — when someone asks "What is [your brand]?", what does the AI say? Is it accurate?

The 90-day GEO programme

Month 1: Entity audit (Wikidata, Knowledge Graph state). Data inventory (what unique stats can you publish?). E-E-A-T baseline.
Month 2: Content rebuild — add stats, citations, author bios, recency markers, schema. Deploy entity improvements.
Month 3: Track + iterate. Identify pages that aren't getting picked up; refactor them. Build co-citation through digital PR.

Common GEO mistakes

Treating GEO like classic SEO. Backlinks help, but they're not the primary lever.
Ignoring Wikidata. Easiest win in GEO — most brands have no entry or an outdated one.
Generic content. Without unique data, you'll always lose to the source with the original number.
Anonymous content. Author bylines with credentials are essential.

Next steps

Our GEO Package (€89) covers the full pipeline: entity audit, content rebuild, schema deployment, and 90 days of citation tracking. For most clients, pairing GEO with AEO via the Combo (€129) is the smartest sequence — they reinforce each other.