AI Search Visibility
Seven surfaces · measured every 30 days
The deals you lose here never show up in your analytics.
Your buyer used to search, scan ten links, and click one of them. Now they ask an assistant and get one answer that names two or three companies. When you are not one of them there is no impression, no click, and no bounce. Nothing lands in your reporting, so the loss is invisible until your pipeline thins out and no one can explain why.
What changed
Search became an answer.
A results page gave every credible vendor a chance to be clicked. An answer gives that chance to two or three. The buyer stops comparing and starts with a shortlist someone else assembled, and you are either on it or you are not part of the evaluation.
The uncomfortable part is the reporting. Every other channel tells you when it fails. Paid tells you the cost per click went up. Organic tells you a ranking dropped. This one tells you nothing, because a mention you never got does not generate a row in any report you own.
So the first job is not content. It is instrumentation. You cannot manage a channel you cannot see, and you cannot prove the work moved anything without a number that existed before the work started.
How it works
Measure first. Then fix.
No recommendations before there is a baseline to argue with
01
You find out where you stand
Measurement
We lock a set of the questions your buyers actually ask, run them across seven search and answer surfaces, and score two things: whether you appear, and whether you get cited by name. Competitors you name get scored on the same questions. Then we re-run the identical set every 30 days.
- A citation rate you can put in a board deck
- Named competitors scored on the same questions, so the gap is specific
- A month-over-month diff that shows movement, not vanity traffic
- The questions where you are invisible, ranked by how much they matter
02
You get named
The fix
Engines cite sources that are unambiguous about who you are, what you do, and who you serve. Most sites fail on entity clarity long before they fail on content. We repair the machine-readable layer, open the retrieval crawlers that matter, and build content written to answer a question rather than to rank for a phrase.
- One consistent entity identity across schema, site, and directories
- Retrieval crawler access audited and corrected, training crawlers separated
- Question-shaped content that engines can quote in a sentence
- Same-day index submission so new work enters the freshness window
From our own runs
What the measurement actually found.
Figures below come from live client runs, not from published industry research. Client identity withheld.
15 of 24
Buyer questions where the client was cited by name
First full run for a services company: 24 buyer questions across seven surfaces. Present on 19, cited by name on 15.
Weeks
Time for new content to earn its first AI citation
Three articles written to match real buyer questions came back as cited sources in AI answers while still holding near-zero Google impressions. Citation arrived before ranking did.
6,489 / 18
Impressions to clicks on service pages that looked broken
The obvious call was to rewrite the page titles. The impressions turned out to be brand navigational, so buyers were never reaching those pages and a rewrite would have changed nothing. We withdrew our own recommendation and fixed the real cause.
Two ways in
Same question. Different work.
Where your buyers are decides which one you need
If your buyers are nearby
Local
Someone three miles away asks an assistant for what you sell, and it names two businesses. The work here is making sure the shared local record about you is complete and says the same thing everywhere, because that record is most of what the assistant is reading.
- Google Business Profile completed, categorised, and kept current
- Name, address, and phone reconciled across every directory listing you
- LocalBusiness schema and service-area markup on your own site
- Review volume and recency treated as a ranking input, not a vanity metric
- Checked against the businesses you actually lose customers to
Often paired with a website rebuild, since a thin site caps the rest
If your buyers are anywhere
Regional and up
Your buyer researches for weeks before a first call, across a category where the answer names two or three firms. The work here is the locked question set, the seven-surface measurement, and the entity and content repairs that move citation rate over quarters.
- Locked buyer-question set scored across seven search and answer surfaces
- Citation rate benchmarked against competitors you name
- Entity, schema, and crawler-access repair across the whole domain
- Question-shaped content aimed at the gaps the baseline exposes
- Re-measured every 30 days so movement is provable
Delivered with Proxigee where the scope calls for a larger team
The engagement
Four phases. One number that moves.
The capability transfers to your team, the measurement continues without us
01
Baseline
We build your question set with you, agree the competitors that count, and run the first measurement. You get a number and a ranked list of gaps before any build work starts.
02
Repair
Entity and schema corrections, crawler access, index submission. This is the work that moves citations without writing a single new article.
03
Build
Question-shaped content aimed at the gaps the baseline exposed, published on a cadence your team can sustain after we leave.
04
Re-measure
Same questions, same surfaces, every 30 days. Comparability is the point, so the set does not drift. New questions go in a marked section and are scored separately.
When the site is the constraint
Sometimes the baseline shows the problem is not content or schema. It is that the site says nothing an engine can quote, because the positioning was never settled. In that case the repair is a rebuild, and we do that work too.
Questions we get asked
Straight answers.
What is answer engine optimization (AEO)?
Answer engine optimization, also called generative engine optimization or GEO, is the practice of getting your company named inside the answers AI assistants generate, rather than ranked in a list of blue links. Traditional SEO competes for a position on a results page. AEO competes to be one of the two or three companies an assistant cites when it answers a buyer's question directly.
How is AI search visibility different from SEO?
SEO produces a click you can measure. AI search visibility produces a mention you cannot. When an assistant answers a buying question without naming you, there is no impression, no click, and no bounce, so nothing appears in your analytics. The loss is real and invisible at the same time. That is why measurement has to be run deliberately against a fixed question set instead of read off a traffic report.
How do I know if ChatGPT or Perplexity mentions my company?
You have to ask them, systematically and repeatedly. We run a locked set of your buyers' questions across Google organic, Google AI Overviews, Google AI Mode, ChatGPT search, Perplexity, Microsoft Copilot, and Gemini, then score whether your company appears and whether it is cited by name. Running the identical set every 30 days is what turns a snapshot into a trend you can manage.
How long does it take to show up in AI answers?
Faster than classic search rankings, and we have measured this rather than assumed it. Content written to answer a specific buyer question has come back as a cited AI source within weeks while still holding near-zero Google impressions. Entity and schema repairs can move citations without any new content. Competitive head terms in classic search still take six to nine months, and we will say so up front rather than let you reach month three expecting attributed revenue.
Does blocking AI crawlers protect my content?
It usually costs you visibility for no benefit, because most teams block the wrong crawler. GPTBot is OpenAI's training crawler. OAI-SearchBot and ChatGPT-User handle retrieval, which is what governs whether ChatGPT can cite you in an answer. Blocking GPTBot opts you out of model training and does nothing to protect you from being quoted, while blocking retrieval crawlers makes you uncitable. We audit the two categories separately and tell you which decision you actually made.
Who is this for?
Two kinds of buyer, and the work is different for each. Local owners around Tampa, New Port Richey, and Pasco County who want to be the business an assistant names when someone nearby asks for one. And mid-market and enterprise operators in supply chain, manufacturing, energy, healthcare, government, and professional services whose buyers research before they ever call. The common thread is that the buying question is specific enough to have a right answer, so being named is worth more than a page of traffic.
Will ChatGPT recommend my business to people nearby?
Only if it can tell where you are and what you do. For a local business the deciding factors are usually a complete and current Google Business Profile, a name, address, and phone number that match across every directory that lists you, LocalBusiness schema on your own site, and recent reviews with real detail in them. Assistants lean heavily on that shared local record. When it disagrees with itself, and it usually does after a rebrand, a move, or a phone change, the assistant tends to name a competitor whose record is clean.
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