Capability · 15
15 · Practice
GEO / AEO
Get cited by ChatGPT, Perplexity, Gemini, and Google AI Overviews, on purpose, not by accident.
Definition
Generative Engine Optimization and Answer Engine Optimization, the discipline of making a brand legible, citable, and recommended when an AI is the first reader. We audit how today's models describe the brand, fix the source material they pull from, and engineer the schema, entities, and canonical narratives so the brand shows up correctly in answer engines, AI Overviews, and the agents shopping on behalf of customers.
For
- 01Brands whose customers now start in an LLM, not a search bar.
- 02Agencies that need a defensible answer to "how do we show up in AI search?"
- 03Studios and IP owners protecting attribution and likeness across model generations.
What we ship
07 sub-practices
Each line below is a standing offer. Scoped, priced, and led by a named practice lead. Mix and match across a brief; nothing here is a one-off.
AI Visibility Audit
Baseline how ChatGPT, Gemini, Perplexity, Claude, and AI Overviews currently describe the brand, its products, and its competitors, with the source URLs each engine is leaning on.
Structured Data & Schema
Schema.org, JSON-LD, llms.txt, canonical entity graphs, and machine-readable feeds so models parse the brand the way the brand intends.
Entity & Knowledge Graph Engineering
Canonical entity graphs, Wikidata and Wikipedia presence, and the reference scaffolding that lets models disambiguate the brand from its category, its competitors, and its name twins.
Source Authoring
Primary research, reference pages, glossaries, and citation-worthy long-form built specifically to be ingested and quoted by answer engines.
Answer Targeting
Map the prompts customers actually ask, then engineer the on-domain pages, FAQs, and primary research that consistently surface as the answer, not just a citation.
Prompt & Answer Engineering
Reverse-engineer the prompts customers use, then engineer the brand surfaces that consistently surface in the answer.
GEO Monitoring & Ops
Ongoing tracking of brand mentions, sentiment, and citations across LLM outputs, with a remediation loop when an engine drifts.
Outcomes
- Accurate, consistent brand representation across every major AI engine.
- A measurable share-of-voice in generative answers, not just SERPs.
- Owned source material the models actually cite back.
Process
- 01Audit: map current AI visibility, citations, and competitive share-of-voice.
- 02Foundations: fix schema, canonicals, llms.txt, and structured feeds.
- 03Authoring: publish the source material the models will quote.
- 04Monitor: track citations and iterate as engines evolve.
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