AI Search / GEO
Getting recommended by AI assistants is a discipline of its own
Buyers increasingly ask ChatGPT, Perplexity, Gemini or Google's AI Overviews for a recommendation — and get one answer naming two or three brands. This is the work of being one of them.
The AI Search Optimisation Framework
Six layers, in order. Skipping to the content layer before the foundation is why most GEO efforts produce nothing measurable.
- 01
Retrievability
Server-rendered content, fast pages, clean architecture. An assistant cannot recommend what it cannot retrieve.
- 02
Entity clarity
One consistent description of who you are and who you serve, mirrored everywhere you appear online.
- 03
Answer-first content
Self-contained passages that survive being quoted out of context, on the questions buyers actually ask.
- 04
Third-party presence
Mentions and citations in the directories, comparisons and publications assistants retrieve from.
- 05
Structured data
Schema that states plainly what you are, what you offer and who stands behind it.
- 06
Measurement
A fixed prompt set, run monthly across the major assistants, logging which brands get named and in what order.
Start here
What is GEO (Generative Engine Optimisation)?
GEO is the practice of getting a brand retrieved, cited and recommended inside AI-generated answers — ChatGPT, Perplexity, Gemini and Google AI Overviews — rather than only in traditional search results.
GEO vs SEO: what actually differs
GEO and SEO share a technical foundation and diverge on three things: the unit of optimisation, the weight given to entities, and how you measure success.
How LLMs discover and choose which brands to name
Three separate mechanisms decide whether an AI assistant names your brand: what was in training data, what live retrieval surfaces, and how confidently the model can identify you as an entity.
Want to know if AI assistants mention you?
The AI-Driven Growth System includes a visibility assessment across the major assistants, plus the entity and content work to change the answer.