GEO
The next layer past AEO. Prompt-shape and topic-shape content for the generative answer surfaces, Google's SGE/AI Overviews, ChatGPT web search, Perplexity, Gemini, that synthesize across multiple sources.
- Engines
- AIO · ChatGPT · Perplexity · Gemini
- Lead time
- 8–12 weeks to first effects
- Companion to
- AEO + SEO
What it is
The plain-English version.
Generative Engine Optimization (GEO) is the practice of structuring content so it gets pulled into multi-source AI-synthesized answers, not just cited as one source. While AEO optimizes for being cited, GEO optimizes for being the source the model leans on most heavily when synthesizing across many. Practical work includes topic-cluster depth, semantic completeness, and entity authority.
What we do
The work, unpacked.
Topic completeness
Map every related sub-topic to the parent question. The model synthesizes from sources that answer broadly, gaps cost you.
Source authority
Original data, original framing, named expert authors. Generative engines weight original analysis over rehashes.
Synthesis-ready structure
Tables, lists, comparison matrices, clearly-labeled definitions, the formats LLMs lift cleanly into synthesized answers.
Prompt mapping
Reverse-engineer the prompts your customers actually use. Map content directly to those prompt shapes.
What you get
Concrete deliverables.
- Prompt-shape content audit
- Topic-cluster gap analysis
- Synthesis-ready content production
- Original data + framing assets
- Monthly GEO citation tracking
- Multi-engine synthesis monitoring
Questions
Common first asks.
AEO targets being cited as a source inside an AI answer. GEO targets being the dominant source the model synthesizes from when combining multiple. Different optimization, related work, we typically do both together.