AI Search Optimization (GEO)
A growing share of buyers ask an assistant before they open a search engine, and the assistant returns one answer rather than ten links. If your content is not structured for a model to read, extract, and attribute, you are absent from that answer no matter where you rank in blue links. We rebuild the pages that matter so the model has something clean to quote, with your name attached.
Being findable is no longer the same thing as being citable.
Classic SEO competes for a position in a list. An answer engine assembles one response from whichever sources parse most cleanly and contradict each other least. That rewards a different shape of content: explicit entities, defined relationships, schema that agrees with the visible copy, and a direct answer placed where an extractor looks first. We build that shape on your priority pages, then track whether the engines actually pull from them — because this discipline is young enough that the honest version of it is measured, not asserted.
Baseline citation testing
Before touching a page, we run your priority queries through the assistants and record who gets cited, what they get cited for, and which source the model leaned on. Everything after this is measured against that baseline.
Entity-rich content restructuring
Priority pages are rewritten around clear entities, definitions, and stated relationships — the structure large language models parse most reliably — rather than narrative copy written only for a human skim.
Schema that agrees with the page
Structured data (Organization, FAQ, Product, HowTo) applied consistently and kept in sync with the visible copy, so a crawler gets one unambiguous version of your content. Schema that contradicts the page is worse than no schema.
Answer-ready formatting
Direct-answer summaries, definition blocks, and comparison tables placed where extraction tools look first. The same content a human skims to, arranged so a model can quote it without mangling it.
- Baseline AI-citation test across priority queries and competitors
- Entity and schema markup implementation on priority pages
- Answer-ready content restructuring (direct-answer blocks, comparison tables)
- FAQ and HowTo schema deployment
- Citation monitoring across ChatGPT, Gemini, and AI Overviews
- Quarterly re-test and restructuring based on what the engines actually cite
The engagement
Probe
Run your priority queries through each assistant and record the current citations. Nothing gets rewritten before there is a baseline to beat.
Restructure
Rebuild the priority pages around entities, schema, and answer-ready formatting, keeping the markup and the visible copy in agreement.
Re-test
Re-run the same probes each quarter, compare against the baseline, and let what the engines actually pull decide the next round of edits.
Common questions
It sits on the same technical foundation but optimises for a different consumer — a model extracting one answer, not a person scanning ten links. Entity structure, schema consistency, and extractability matter more; keyword density matters less than it ever did.
No, and anyone who does is selling you something. Our control ends at the structure of your content. The retrieval and ranking behaviour of a model we did not build is outside it. What we commit to is the structural work, a measured baseline, and quarterly re-testing so you can see whether it is moving.
It is early, which is exactly the argument for a measured approach rather than a large one. We recommend starting with your highest-intent pages, establishing a baseline, and expanding only where citations actually move.