By Mark Buraga, Independent SEO Consultant at Growth Engine PH
Last updated: 26 August 2026
Fifteen fixed prompts, eight captures, one engine. On the five prompts that name our practice, we were cited 39 times out of 40. On the eighty prompts a client would actually type, five. Same weeks, same method, same domain.
Both figures are our AI search visibility: 97.5% on one half, 6.25% on the other. Only one of them is worth reporting to a client, and it is not the first.
One finding should decide what you do with your own reporting. All five buying-intent citations came from one prompt, and the prompt has our name in it. Strip it out and the honest count across eight captures is 0 of 72.
We measured our own domain first because a score you cannot decompose is a score you cannot trust, including ours. What follows is the whole measurement, run with Google AI Mode between 13 June and 10 August 2026: the prompt set, the dates, the holes in the run, and both halves side by side.
What did eight captures of AI search visibility actually show?
Across eight captures of the same fifteen prompts on Google AI Mode between 13 June and 10 August 2026, Growth Engine PH earned a citation on 39 of 40 brand-direct prompts and 5 of 80 non-brand prompts.
The prompt set is fixed and unchanged since 13 June 2026: five brand-direct prompts naming the practice or its founder, five service-intent prompts a buyer would type, and five competitor-comparison prompts. We ran every capture through Google AI Mode, signed out, on the Philippine default geography. The prompts use buyer vocabulary rather than ours, including the word “agency”, because the question is what surfaces when a buyer types what buyers actually type.
One honest gap in our data before the numbers. These are eight captures spanning ten calendar weeks, not eight consecutive weeks, because two weeks in late June were missed. Set, engine and geography held constant throughout, so the comparison is still like for like. For the on-page half measured on your own site, run the diagnostic.
Both halves of the score, capture by capture
The brand half is the count of brand-direct prompts that produced a citation, and the buying half is the same count on non-brand prompts. Both are printed below from our data, capture by capture, so the composition of our AI search visibility is auditable rather than asserted.
| Capture | Brand-direct cited | Non-brand cited | Total sources surfaced |
|---|---|---|---|
| 13 June 2026 | 5 of 5 | 1 of 10 | 281 |
| 29 June 2026 | 5 of 5 | 0 of 10 | 57 |
| 6 July 2026 | 4 of 5 | 0 of 10 | 148 |
| 13 July 2026 | 5 of 5 | 0 of 10 | 65 |
| 20 July 2026 | 5 of 5 | 1 of 10 | 113 |
| 27 July 2026 | 5 of 5 | 1 of 10 | 127 |
| 3 August 2026 | 5 of 5 | 1 of 10 | 230 |
| 10 August 2026 | 5 of 5 | 1 of 10 | 243 |
| Eight captures | 39 of 40 | 5 of 80 | 1,264 |
Read the two middle columns as two different experiments. The brand half moved once in ten weeks, a single miss on 6 July 2026. The buying half sat at zero for three consecutive captures and never rose above one. The right-hand column carries a separate caution worth one sentence: the same fifteen prompts surfaced 57 sources one week and 281 another, so any AI search visibility figure compared across weeks is riding on a denominator that moves.
Why can one AI search visibility score not answer two questions?
A brand-direct prompt tests whether an engine can resolve your entity, while a buying-intent prompt tests whether the market has said enough about you to be recommended, and passing one says nothing about the other.
Entity resolution is won on surfaces you own. A canonical name used consistently, a founder who exists as a resolvable person rather than a byline, and schema that states plainly what a page is and who stands behind it: these are the AI Mode signals that let an engine confirm you when someone types your name. Most of that work ships in a fortnight, which is how a practice with no off-page footprint posts 39 of 40.
Consensus cannot be published into existence. Market consensus is assembled from what other people have already said about you, in places you do not control, which is why it lags every on-page fix by quarters rather than weeks. For example, ranking versus citation come apart routinely: position on a results page is not the input the answer layer reads.
The two halves in our own numbers
We pass the entity test at 39 of 40 and the consensus test at 5 of 80, in the same captures, on the same domain, in the same weeks: 97.5% against 6.25%. Averaging the two produces a figure that describes neither.
The direction is corroborated outside our own data by Semrush and Kevin Indig, whose ghost citations study of 9 June 2026 covered 3,981 domain appearances across 115 prompts, 14 countries and 4 engines. It found 74.9% of appearances carried a citation while only 38.3% carried a brand mention, and that commercial queries produced a 35.6% mention rate against 18% for informational ones.
Query type is the variable that moves the result at scale. On our single domain, in practice, it moves the result by a factor of fifteen. If your dashboard reports one AI visibility figure, the wrong half is probably the half you are reading.
Which prompt produced the five citations we did win?
All five of our non-brand citations came from a single prompt, “Growth Engine PH alternatives”, which the prompt set files as a competitor query and which behaves as a brand query, leaving 0 of 72 across eight captures once it is removed.
On 10 August 2026 I ran the eighth capture of the same fifteen prompts I had been running since June. Prompt 13 came back with eleven sources: agencylist.com, outsourceaccelerator.com, seo-hacker.net, leapoutdigital.com, a Philippine content-marketing roundup, and growthengineph.com sitting among them. Cited again. That made five buying-intent citations across the run, and 5 of 80 had started to feel like the beginning of a curve.
Then I lined the five up. Same prompt. Every time. Not five prompts producing one citation each, one prompt producing all five, and that prompt had the practice’s name in it.
What the first capture had already recorded
The classifier is the piece of our capture that files each prompt into a bucket, and on day one it filed the alternatives win as a brand query. In our first capture, dated 13 June 2026, the totals block reads brand_query_citations: 6, nonbrand_query_citations: 0, while the per-prompt records underneath say five brand-direct and one competitor. Written into the same file, four lines up, sits the note: “Brand named (query is brand-anchored).”
The data had been right for eight weeks. Our analysis had been wrong, because the bucket label said competitor and nobody read past it. The number was never the problem. The composition was, and it was sitting in plain text the whole time.
Generalize the shape and it gets uncomfortable fast. “[Brand] alternatives”, “[Brand] vs”, “[Brand] reviews” and “is [Brand] any good” are entity tests wearing commercial labels. Every tracking tool on the market files them as competitor queries. Every one of them is typed by a searcher who already knows your name.
What gets cited when the query has no brand in it?
Across our ten buying-intent prompts, 568 distinct domains appeared, with reddit.com cited 34 times and linkedin.com 24 times, each on 8 of the 10 prompts.
Neither of those two is a property any firm owns, and that is the whole reason the halves do not converge on their own. A brand-direct answer is assembled largely from our own site and our founder profile, both of which we can edit this afternoon. A buying-intent answer is assembled from a forum, a professional network, a scatter of directories, and a long tail of roundups, none of which take instructions from us.
So the operator who sets out to improve AI search visibility by improving the website is improving the half that was already fine. For example, our data shows the two surfaces deciding our buying half are the two we cannot publish to at will. The mechanism is documented already and worth reading first: Reddit earns mentions for reasons a firm cannot manufacture.
What is the strongest case against reading our numbers this way?
Shopify reported that AI-driven traffic and orders tripled year over year in Q2 2026, which is real evidence that AI search produces commercial outcomes at scale for sellers who have already passed the consensus test.
State it at full strength, because it is the objection I would raise. Growth Engine PH is a young practice with a thin off-page footprint, exactly the profile that would fail a consensus test regardless of how AI search behaves, so 5 of 80 may be a fact about us rather than about the mechanism.
The named counter-evidence is substantial. Shopify’s president Harley Finkelstein told TechCrunch on 5 August 2026 that AI-driven traffic and orders tripled year over year in Q2, that 50% of AI-referred sessions land directly on a product page, roughly 2.5 times the traditional-search rate, and that AI has become “a complement to search, rather than a substitute for it”, with traditional search sessions themselves up 1.3x over two years.
Both findings are true, and they describe different halves
Shopify measures what happens after an AI answer routes a buyer to a product page, across a merchant base with millions of product URLs, deep third-party review coverage, and product schema at scale. That population has already passed the consensus test. Our data measures whether the consensus test is passed at all, on a domain that has not. Shopify’s own framing supports the split rather than contradicting it: AI as a complement, with 75% of AI-attributed purchases occurring outside the top 100 categories, describes a long tail being surfaced by third-party signal.
The limit on our side stands and belongs in print. One domain, one engine, one geography, fifteen prompts, eight captures, run through Google AI Mode in 2026. This is a demonstration that the two halves can diverge sharply on a real domain, not a measurement of how often they diverge across the market. You do not need our result to generalize. You need to run the split on your own prompt set.
The objection that this is already known
The second objection is that the exclusion rule is old news. Authority Tech published it on 13 March 2026: exclude branded prompts unless you are running a separate entity-resolution test. Fair, and correct.
The rule exists in methodology writing and is absent from the artifacts buyers are shown. The two free checkers with the largest reach both report a single blended figure. A standard nobody applies at the point of reporting is not a standard the buyer benefits from, and knowing to split is not the same as having seen what the split looks like on a named domain.
How to split your own AI search visibility score
Label every prompt in your tracked set by whether the query text contains your brand name, report the two halves separately, and never average them.
- Sort by the query text, not by the bucket your tool assigned. A bucket label is a convention written by a vendor; the string the searcher typed is a fact. Anything containing your name goes in the brand half, including the alternatives, versus, reviews and “any good” shapes.
- Report both halves every time, with the weak one first. A brand half at 90% and a buying half at 5% tells you where the next quarter of work goes. A blended 30% tells you nothing, moves for reasons nobody can name, and keeps moving while the pipeline sits flat.
- Hold the instrument still. One fixed prompt set, one engine, one geography, one cadence, or the week-to-week comparison is not a comparison. In practice, a small set held constant beats a large set that changes between runs.
Send me your prompt set and I will split it and tell you which half your number is made of: split your prompts and the two halves come back to you.
What are we doing about our own 5 of 80?
We are treating the buying-intent half as a consensus problem rather than a page problem, which means off-site work on the surfaces that were actually cited, and we will publish the next eight captures whether or not the number moves.
The work is unglamorous. Show up in the forum and network threads the engine is already reading on our buying prompts. Get the directory and roundup entries that surfaced on prompt 13 in 2026 to describe the practice accurately. Put original measurement rather than opinion into public. None of it is fast, which is why we measured it apart from the on-page half that was fixed in a fortnight.
Growth Engine PH runs this fixed-prompt AI visibility capture as a standard deliverable on every retainer tier, splits each result into a brand half and a buying half, and reports both. Split the number before you try to move it. Half of it was never in doubt, and the other half is the only half that sells anything. Ask your vendor which prompts the number is made of. If the answer takes more than a minute, that is the answer.
Frequently Asked Questions
Our page ranks top 5 but is not cited in the AI answer. What do we check first?
Check the query first, not the page. If the query contains no brand name, the miss is usually a consensus problem rather than a page problem, and the fix is off-site, on the surfaces other people write on. If the query does contain your brand name and you are still missing, the miss is an entity-resolution problem and the fix is on-page: canonical naming, Article and Person schema, and a founder who resolves as a real entity. The two failures look identical in a dashboard.
What does each AI engine want before it cites you?
Engines differ, and the differences are measured rather than assumed. Kevin Indig’s framing in the 9 June 2026 Semrush study is that “different engines reward different signals, formats, and sources”, with citation rates in that dataset running from 21.4% on Gemini to 87% on ChatGPT. Our own corpus covers a single engine, Google AI Mode, across eight captures, so it says nothing about the others. Treat any single-engine measurement, ours included, as evidence about that engine.
What happens after you get cited?
It depends entirely on which half produced the citation. A brand-direct citation services demand that already existed: someone typed your name, and the engine confirmed you. A buying-intent citation is the only kind that can create demand, because the searcher did not know you before the answer loaded. Even then the name may not survive. Roughly 40% of AI citations omit the source brand entirely across some 16 million brand appearances, per the analysis Search Engine Land reported on 29 July 2026, rising to 52% on Perplexity.
Is a query like “[brand] alternatives” a branded query or a competitor query?
The query text decides, not the bucket label. If your brand name is in the string, the searcher already knows you exist, so the query tests entity resolution rather than market consensus, whatever your tracking tool calls it. Every one of our five non-brand citations across eight captures came from exactly this shape, and removing that one prompt took our buying-intent result from 5 of 80 to 0 of 72. Sort your own set by query text this week.
How many prompts do I need before the split means anything?
Fifteen is enough to see a split this size and not enough to size a market. The value comes from holding the set still: fix the prompts, fix the engine, fix the geography, and compare the same set to itself over time. A larger set that changes between runs is worse than a small set that does not, because a changing set makes every movement uninterpretable. Cadence matters too, and our own two missed weeks are visible in the series.
My vendor reports one AI visibility percentage. What should I ask for?
Ask for the prompt list, and ask for the two halves reported separately with the non-brand half first. If the prompt list is not available, the percentage is not auditable, and an unauditable number should not be steering budget. A vendor who can produce the list in a minute is running a fixed set. A vendor who cannot is either blending brand and buying prompts or changing the set between reports, and both make the trend line meaningless.
Sources
- Growth Engine PH AEO corpus: eight captures of a fixed fifteen-prompt set on Google AI Mode, 13 June to 10 August 2026, recomputed 15 August 2026. Own data.
- Semrush and Kevin Indig, Ghost Citations study, 9 June 2026.
- Search Engine Land, ghost citations, 29 July 2026, Nikki Lam and Samanyou Garg.
- TechCrunch, Shopify and AI search, 5 August 2026, Sarah Perez.
- Authority Tech, Share of Citation, 13 March 2026.
- r/content_marketing thread, 10 August 2026.
- r/B2bGotomarket thread, 10 August 2026.