The GEO Framework

Generative Engine Optimization is the practice of becoming the source a generative engine uses when constructing an answer. It overlaps with SEO and AEO but adds three concerns specific to generative systems: how the model retrieves your content, how it represents your entity in the absence of retrieval, and how it weights your content against alternatives during generation.

GEO is not 'getting cited.' Citations are an artefact. The substrate question is: did the model use your content to construct the answer, regardless of whether the citation surface displays you?

Pillar 1: Substrate Quality

The model has to ingest something good. This is the AEO layer: chunked, fact-dense, hedge-disciplined, version-stamped content. Without substrate quality, the rest is irrelevant.

Pillar 2: Citation Surface

When the model does cite, can the surface display you? This is where canonical URLs, opengraph metadata, favicon visibility and brand-name disambiguation matter. A model that wants to cite 'Toronto SEO' as a source needs to know which entity — the agency, the city's SEO industry, or the generic phrase — is meant.

Pillar 3: Retrieval Friendliness

RAG-based systems retrieve before they generate. Pages with strong heading semantics, clean HTML, and topical clustering retrieve better. Pages buried under aggressive JavaScript or behind auth retrieve poorly. We have seen single-digit citation rates lift to 30%+ purely by improving retrieval — same content, no copy changes.

Pillar 4: Brand Conditioning

Models also generate without retrieval. In those cases, the model is drawing on what it learned about your brand at training time. Brand conditioning is the long-tail work: getting cited in third-party publications, having a consistent entity description across the web, and maintaining a Wikipedia or Wikidata presence (where editorially appropriate). Brand conditioning compounds slowly and is the deepest moat.

Pillar 5: Negative Reduction

Bad content actively hurts. A site with 100 thin programmatic pages and 10 strong pillars will be evaluated by the average. Removing the weak content can lift the perceived authority of the rest. We typically see negative reduction outperform positive expansion in the first 90 days of an engagement.

GEO measurement is harder than SEO measurement because there is no Search Console for ChatGPT. We use a fixed query basket: 20–80 queries representative of the categories the client wants to win. Each month, we probe a fixed set of LLMs (typically ChatGPT-4o, Claude Sonnet, Gemini Pro, Perplexity, Google AI Overviews) and record three things per query: was the brand cited, was a competitor cited, and how prominent was the placement (lead source, supporting source, or absent).

The trend matters more than the absolute number. A 22% share of voice that rises to 28% in three months is a successful engagement. A 40% share of voice that drifts to 35% over the same period needs investigation.

1. 1Weeks 1–2: Build the query basket and run the baseline probe across all five LLMs.
2. 2Weeks 3–4: Negative reduction — identify and remove or merge the weakest 10–20% of indexed URLs.
3. 3Weeks 5–8: Substrate quality pass on the top 20 traffic URLs (apply the AEO eight-point checklist).
4. 4Weeks 9–10: Retrieval friendliness audit — heading semantics, JS-rendered content, robots.txt rules, sitemap completeness.
5. 5Weeks 11–12: Brand conditioning push — pitch three data-led stories to journalists with our /resources/prompts citation prompt.
6. 6Repeat the LLM probe at weeks 6 and 12. Compare to baseline.