Generative Engine Optimization — Built on How LLMs Actually Decide What to Cite
GEO isn't "SEO for ChatGPT." It's a distinct discipline grounded in how large language models retrieve, evaluate, and cite sources across web search, training corpora, and knowledge bases. We build GEO programs that produce citations in ChatGPT, Perplexity, Gemini, Google AI Overviews, Claude, and Copilot — engineered from first principles, not from vendor playbooks or LinkedIn threads.
First-principles · Anti-hype · Prompt-level measurement
What GEO Actually Is — and What It Isn't
GEO is the discipline of engineering your brand, content, and authority signals so that large language models — when answering queries relevant to your business — cite you, recommend you, or surface you as a trusted source. It spans ChatGPT (with and without web search), Perplexity, Gemini, Google AI Overviews, Claude (with web tools), Copilot, and the emerging vertical AI-search tools.
GEO isn't:
The most important thing to understand
LLMs don't rank pages the way Google ranks pages. They generate answers from a combination of training data (what the model learned), retrieval-augmented context (what it searches the web for in real time), and structured signals (schema, knowledge bases, citations). Optimizing each surface means understanding which combination the model is using and engineering the inputs accordingly. If your GEO partner can't explain the difference between training-data and retrieval-augmented citations, they're guessing.
How AI Search Engines Actually Decide What to Cite
Training-data citations
ChatGPT without web, base-model Claude. The model cites what it learned during training — a fixed corpus up to a cutoff. A citation here is permanent and doesn't refresh. Your brand has to be present in the underlying data: Wikipedia, Wikidata, established publications, the news cycle and forums the model crawled.
Retrieval-augmented citations
ChatGPT with web, Perplexity, Gemini, Claude with web tools, Copilot. The model retrieves and ranks sources in real time, then synthesizes a citation. Closer to traditional ranking but weighing authority, recency, citation density, source diversity, schema clarity, and extractability.
Knowledge-base & structured-data citations
Google AI Overviews, knowledge panels. For factual queries, models draw from Wikipedia, Wikidata, schema-marked pages, and the Knowledge Graph. If your entity isn't in these structured sources, you can't be cited for the queries that draw from them.
Community & citation-graph citations
LLMs have absorbed citation patterns from Reddit, Quora, Stack Exchange, and niche forums. Brands mentioned frequently here have stronger baseline training-data presence and stronger retrieval-augmented authority.
The synthesis layer
Beyond any source, LLMs decide what to cite based on the structure of the answer they generate. A clean, well-structured source for an extracted fact is more citable than a wall of prose. Extractability is one of the most underappreciated GEO factors.
A GEO program engineers for all five surfaces simultaneously. Most "GEO" advice online addresses only one (usually Surface 2, because it's the most visible).
Where AI Search Gets Its Answers
Your Own Properties
Website, blog, docs, knowledge base, schema. The highest-leverage move — you control everything. We engineer clean definitions, FAQ structure, expert attribution, schema, and citation-worthy statistics.
Third-Party Authoritative
Wikipedia, Wikidata, Crunchbase, analyst reports (Gartner, Forrester), trade publications, news, academic citations. Determines whether the model treats you as a named entity worth citing.
Community Sources
Reddit, Quora, Stack Exchange, HN, niche communities. Mentions here shape training-data perception and retrieval authority. Engineered through genuine engagement — not astroturfing, which models increasingly detect.
News & Current Events
The news cycle the model trained on and the live feeds the retrieval layer pulls. We coordinate with PR to engineer coverage that produces durable presence, not short-term spikes.
Structured Data & Knowledge Bases
Schema, Wikidata, the Knowledge Graph, Bing's index, vertical bases (G2, Healthgrades, Avvo). One of the highest-ROI GEO moves for any brand. We audit, build, and maintain it.
A mature program weights effort across all five categories based on your industry, competitors, and the queries that matter most.
The Four Levers We Tune
Extractability of your content
We restructure priority pages so the answers the model wants to cite are clean and atomic — clear definitions up top, FAQ structure, schema that surfaces the right facts, verifiable author attribution, and inline-sourced statistics. The page should read like a good answer to a question.
Authority signals across the citation graph
We audit your presence in Wikipedia, Wikidata, the news cycle, the analyst ecosystem, the trade press, and community surfaces. Where you have presence, we strengthen it. Where you don't, we engineer it through legitimate channels.
Brand entity & structured-data engineering
We treat your brand as an entity the model must recognize — Wikidata creation and maintenance, sameAs schema linking your properties, organization markup, knowledge-panel optimization, and the structured-data footprint that makes you citable for factual queries.
Measurement & iteration
We measure AI-search citations as a first-class KPI across ChatGPT, Perplexity, Gemini, and AI Overviews — which prompts cite you, which mention competitors but not you, and how the footprint changes. The landscape shifts every quarter; measurement tells us where to focus.
What We Measure
What we measure as success
- AI-search citation rate for the queries that matter — of the 30–100 prompts we track, what % cite or mention you?
- Citation sentiment and context — recommended answer, default, alternative, or source for a fact?
- Share-of-citation vs. named competitors.
- Source diversity — own properties vs third-party (both is healthier).
- Citation refresh rate — refreshing as models update, or stale and decaying?
What we don't call success
- Generic "AI visibility scores" from tools with undisclosed methodology.
- Sentiment scores for prompts no buyer would type.
- "Mentions in ChatGPT" without context on citation vs hallucination.
- Citation counts that include model hallucinations (a named brand without real knowledge isn't a win).
If your partner can't produce a prompt-level citation report tied to the queries that matter, the work isn't engineered for outcomes.
What We Won't Do
GEO is full of vendors selling shortcuts that don't work or don't last. We won't:
Our Process for GEO Engagements
Citation Audit
Weeks 1–2. A structured prompt battery across ChatGPT, Perplexity, Gemini, AI Overviews, and Claude. We score citation presence, sentiment, source, and context vs the competitors that matter.
Source & Content Strategy
Weeks 2–3. We map citation gaps to the five source categories — extractability, third-party presence, structured data — and prioritize by impact and feasibility.
Engineering
Months 1–3. Content restructure, schema deployment, Wikipedia/Wikidata work, third-party placement, news coordination, community engagement.
Measure & Iterate
Month 4+. Re-run the prompt battery monthly. Track citation gain, source diversification, and competitor share-of-citation — iterating on what the landscape is doing, not a vendor playbook.
Three Structures for GEO
GEO Audit & Strategy
A current-state assessment and a 6-month engineering roadmap without committing to ongoing work. Project-based over 4–8 weeks.
Ongoing GEO Program
For brands treating AI-search citations as a real KPI — content engineering, third-party placement, structured-data work, and monthly measurement.
Embedded GEO Strategist
For teams with in-house content and PR that want senior GEO strategy and coordination — for defined senior hours.
GEO Questions
Ready to Engineer AI-Search Citations From First Principles?
Request a GEO audit — yours in about two weeks, with a prompt-level citation report benchmarking you against the competitors that matter.
Request a GEO Audit