Four steps. Fully measured.
How we move a brand from unseen to the name AI says first — and prove it with a number every week.
AEO and GEO work because AI answers are built from sources a model trusts and content it can extract cleanly. We measure where AI currently mentions you, restructure your site so it is easy to quote, build the outside signals that make AI believe you, and re-measure on a fixed schedule. The deliverable is not a dashboard — it is a weekly number: how often AI names you, and how it describes you.
Search used to end on a results page. Increasingly it ends in an answer — one synthesized reply from ChatGPT, Perplexity, Claude, Gemini, or Google’s AI Overviews, with a handful of cited sources and, often, a single recommended name. Ranking #1 on Google does not put you in that answer. Different surface, different signals. Our method is built for the answer.
Listen
We start by finding out what AI already says about you — because you cannot improve a number you have not measured.
What we do
We build a question set from the queries your customers actually use — navigational (your brand name), informational (the problem you solve), commercial (purchase intent), and comparison (you versus a competitor). We then run that set through the major answer engines, sampling each question several times because AI answers are non-deterministic — the same prompt can return different brands on different runs.
What you get
A baseline: your starting mention rate per question, a record of how the model frames you when it does mention you, and a map of which competitors own the answers you want. Every later result is measured against this.
Shape
Then we give AI clear, trustworthy reasons to choose you — on the surface you control: your own site.
What we do
We restructure content for extractability: short self-contained answers near the top of a page, plain definitions of the terms in your category, comparison tables, and FAQ blocks phrased the way people actually ask. Underneath, we add the structured data — Organization, Service, FAQ, HowTo, and entity markup — that tells a model what you are and how you relate to the things it already knows.
Why it works
Language models reward clarity over keyword density. They lift short, factual passages they can quote without risk. A page written to be extracted — not just ranked — is far more likely to become the sentence AI repeats.
Build
On-site work makes you quotable. Off-site work makes you believed. This is the step most teams skip — and the one that moves the number most.
What we do
We earn the third-party signals AI uses to confirm you are a real, authoritative entity: mentions on the platforms models lean on (industry publications, Reddit, YouTube, LinkedIn), entity records (Wikidata, Crunchbase, directories), reviews on the sites in your category, and original data or research worth citing. Recent analysis finds that brand mentions correlate with AI citation roughly three times more strongly than backlinks do — so we treat “being talked about” as the core asset.
What you get
A widening footprint of places that corroborate your brand, so that when a model assembles an answer, the evidence points to you from several directions at once.
Track
Finally, we re-measure — on the same question set, on a fixed cadence — so progress is never a feeling. It is a delta.
What we do
We re-run the baseline questions weekly and report two things in plain English: your current mention rate versus day one, and any change in how AI frames you. When a competitor gains or loses ground, you see it. No dashboard to log into — a short report that says what moved and what we are doing next.
What we measure
Four numbers carry the story. Each is defined in full in the glossary.
How long it takes
On-site structure and schema can change how a model reads you within weeks. Off-site authority — the part AI trusts most — compounds over a quarter. Because we baseline on day one and report weekly, you see direction long before the full result arrives.