GEO Optimization Fundamentals

What GEO Actually Optimizes For

GEO work increases a brand's visibility in LLM-generated answers (ChatGPT, Perplexity, Google AI Overviews, Gemini, Copilot). The indirect success metric is growth in traffic from LLM sources, tracked via GA4. GEO overlaps heavily with SEO — the same technical foundation, topical authority, and entity structure matter — but the target output is different: appearing in an AI answer, not ranking #1 in blue links.

The Process, Step by Step

1. Technical SEO check — indexation and rendering issues block both search engines and LLM crawlers equally; this must be clean first.
2. Brief niche analysis — establish precisely who the business and audience competitors are.
3. Define the central entity/entities — the core product or topic every piece of content should reinforce.
4. Build the SILO — analyze which topics already drive competitor traffic, then supplement using Wikipedia, PAA, and other entity-collection sources.
5. Build the content plan from the SILO — each entity in the graph becomes one content-plan article carrying its own core intent, further decomposed via Query Fan-Out.
6. Convert intents to prompts — for each intent, generate the natural-language prompt a real user would type into an AI system, then track brand visibility for that prompt set using a tool like Peec.

What Query Fan-Out Actually Means

Query Fan-Out is how modern AI search handles a complex question it can't answer from a single lookup: it silently splits the question into several narrower sub-queries, searches each one, and merges the results into one answer. Ask "what's the best camera phone under $500 in 2025?" and the system might fan that out into "best camera phones 2025," "phones under $500," "iPhone vs. Samsung camera comparison," and "smartphone camera tests" — all in the background, in seconds, invisible to the user. This is exactly why content needs to cover the full set of sub-intents around a topic, not just its headline query — an AI system doing fan-out on a topic will only find and cite content that actually answers the sub-question it generated.

The About Page Is Disproportionately Important

LLMs typically scan the About page to determine which entities a brand should be associated with. A clear, factual About page directly shapes which entities the AI links the brand to — and which it correctly excludes.

Formation of LLM Consensus (Link-Based GEO Work)

The goal here is narrower than link building for rankings: make it more likely that LLM systems (ChatGPT, Gemini, Claude, Perplexity, Copilot) mention or recommend the brand when answering topical or commercial questions, and build a stable "consensus" in the model that this brand is an authoritative or preferred provider in its niche. Start by identifying the actual queries where a vendor recommendation is natural — "best service for X," "where to order X," "who helps with X." Beyond on-site content, GEO includes building an external "consensus" that the brand is authoritative: 30-50+ external publications where the brand appears in top-10 lists and rankings, in analytical/educational articles ("when to use," "how to choose," "mistakes when picking a vendor"), or in neutral pro/con discussions about hiring professionals — mixing anchored and unanchored (brand-name-only) mentions, placed on trusted sources likely to be part of LLM training data (news outlets, blogs, Q&A platforms, industry directories, media), all reinforcing one consistent narrative about the brand rather than contradicting each other.

Measuring and Reporting

Measure at least monthly via Peec (or a comparable tool), supplemented by direct GA4 tracking of LLM referral traffic and, where possible, server-log analysis of AI-bot user agents. Frame the value to clients precisely: "we are not promoting specific pages to the top of an AI answer — we are building brand authority so AI systems recognize and recommend the brand," and note this work also benefits classic search engine rankings.