Answer Engine Optimization
There is exactly one position in an AI answer. When someone asks ChatGPT which vendor to use, they do not receive ten options ranked by relevance — they receive a paragraph naming two or three. Everyone else is absent, and absence in this channel is not a lower ranking. It is invisibility.
The signals that decide inclusion are not the ones that decided rankings. Across large-scale citation analysis, backlink volume shows slightly negative correlation with AI citation frequency, and so does total site traffic. Word count barely registers in ChatGPT at 0.047. What does correlate is structural: content shaped so passages extract cleanly, entities a model can resolve with confidence, and corroboration from sources the retrieval layer actually reaches.
That last point is where most engagements are won or lost. An answer engine synthesizing a recommendation is reproducing a consensus. Consensus is built in public — in press, in communities, on review platforms, in the sites of the partners you keep. It cannot be marked up into existence.
We work the whole stack: measurement first, then the technical and entity foundation, then content architecture, then the earned-mention program that makes the rest credible. Then we measure again every month, because between 40% and 60% of AI citations change in that window.
What you actually receive.
Prompt-space map and baseline
50 to 200 real buyer questions, run across five answer engines, with citations, mentions, and recommendations tracked separately.
Competitor share of answer
Which brands are being recommended instead of you, on which prompts, and from which cited sources.
Citation architecture
Question-phrased headings, answer-first blocks, self-contained semantic chunks, semantic URLs, comparison content.
Entity and schema graph
Connected Organization, Person, Service and Article nodes with stable identifiers and consistent sameAs references.
Answer library
A growing set of pages that answer the specific questions your buyers ask machines, structured for extraction.
Monthly multi-engine scorecard
Fixed prompt set re-measured monthly, share of answer tracked against named competitors, citation losses flagged for refresh.
Common questions.
What is the difference between AEO and SEO?
SEO competes for position among ten blue links on a results page. AEO competes for inclusion inside a single synthesized answer where only two or three brands are named. Both depend on crawlability and indexation, but the ranking signals diverge: comparative and list-format content earns 25.4% of AI citations while homepages earn 3.3%, and backlink volume correlates slightly negatively with citation frequency.
Which answer engines does AIGNCI track?
ChatGPT, Perplexity, Google Gemini, Claude, and Google AI Overviews. Tracking all five matters because only 11% of domains appear in both ChatGPT and Perplexity, so measuring one engine tells you very little about the others.
How is AEO performance measured?
Against a fixed prompt set re-run monthly. We track three separate outcomes: citations, where your domain appears as a linked source; mentions, where your brand is named without a link; and recommendations, where you are named as a preferred option. We also isolate AI referral traffic and conversion from organic.
Tell us what you are building.
If the question is about discovery, reputation, a new site, or a market move, send the context. AIGNCI will tell you whether an Audit, a build, or a more focused engagement is the right starting point.