One AI visibility number averages engines that read different sources, so it cannot tell you which engine to work on. In our own B2B marketing category, Reddit appears in 22% of cited answers when ChatGPT, Google AI Mode and Claude are added together. Read separately, Google AI Mode cites it in about 36%, ChatGPT in 12% and Claude in none.
Empact Partners, a go-to-market consultancy working inside B2B software teams since 2020, built the Empact Panel to measure this for our partners and for ourselves. Every week it puts 500 buyer questions about the work we sell to ChatGPT and Google AI Mode, puts 50 of them to Claude, and keeps every source each answer cites.
The figures here are our own category, four weekly runs from October 4 to 23, 2026, read engine by engine, then by question, then by source, until the reading says which engine to work on first. We get partners named in the answers AI engines give their buyers, and that is our Generative Engine Optimization (GEO) work. It starts with this reading.
A Blended AI Visibility Number Moves When No Engine Changed
A dashboard that blends engines adds every answer from every engine, counts the ones that cite Reddit and divides. The result is a weighted average, and the weights are how many answers each engine contributed, which is a budget decision somebody made.
Our panel asks Claude a tenth as many questions as the other two, because a Claude answer costs 21 times as much to collect. So the pooled figure leans on ChatGPT and Google AI Mode: 22.4% of 4,124 cited answers.
Give Claude as many answers as each of the others, with no engine behaving any differently, and the blend reads 15.7%. Count each engine’s weekly median once and it reads 15.9%. Same answers, three blends, and none of them is a reading of an engine.
Every week the blend sat in a band no engine visits. Google AI Mode moved between 30.4% and 37.5%, ChatGPT drifted from 13.3% down to 11.2%, and Claude stayed at zero, while the blend described a reader who uses all three engines in exactly the proportions our budget chose.
Our own panel can pool engines into one view, and the operating notes we keep for it treat a change to Claude’s sample size as a change of instrument, because it moves the pooled number while no answer changes. That is the rule this method starts from: each engine is read on its own, and a combined figure is a range with each engine named.
Read Each Engine as Its Own Panel
Reading one engine honestly comes down to a few settings. Each one changes the number, so they are written down before the first run:
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Same questions, same weekday. Ask every engine the same questions, in separate runs, on the same day each week. A question asked on Monday in ChatGPT and on Thursday in AI Mode compares two days as much as two engines.
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A denominator for each question. “Are we named?” divides by every answer collected, because an answer from memory that names you is what the buyer read. “Which sources does this engine read?” divides by the answers that cited at least one source.
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Keep the zero. An answer that cited sources, none of them the one you track, is a zero, and it stays in the count.
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Log the missing apart. An answer with no sources, a run that failed and a collection you refused each get their own column, and none of them counts as a zero.
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A weekly median per engine. Report the median of the last four weekly readings for each engine, and count a move only when it is bigger than that engine’s own week-to-week band.
The third and fourth steps are where per-engine numbers usually go wrong. In our first week, 43 of ChatGPT’s 500 answers cited nothing at all. Counted as zeros, they put Reddit’s share at 12.2%. Left out, it reads 13.3%, the honest answer to what ChatGPT reads, since an answer with no sources says nothing about Reddit.
Claude is the opposite case. All 196 of its answers in the count cited at least one source and none cited Reddit, so they are 196 recorded zeros, and they stay. One Claude question never produced a cited answer in four weeks, and it sits outside the count as missing.
The week our ChatGPT answers came back with no sources
The worst version arrived in our second week. The service collecting our ChatGPT answers changed its scraper, ChatGPT stopped running its web search, and every answer came back with no sources. Logged as zeros, that week would have said ChatGPT had stopped citing Reddit.
Our panel refuses a collection with no citations in it, so that run was thrown out and collected again through another provider. The rerun had sources in all 500 answers.
What a practitioner on Reddit says about the search behind each run
“One thing I’d add to the five runs per engine: log whether the answer actually triggered a web search. A run that answers from the model’s own memory and a run that searched and cited pages are measuring two different things, and averaging them blurs both. ChatGPT in particular doesn’t search on every prompt, so the same question can flip between the two from one run to the next.” — u/marintkael, r/TechSEO, Oct 2026
Each row of the log carries these fields, whether a panel fills it or a person does it by hand:
| Field | What to record | Why it changes the reading |
|---|---|---|
| Question and group | The exact wording, and the group it belongs to | An engine’s number moves with the mix of questions behind it |
| Engine and surface | The product and how you reached it: for us, ChatGPT’s web app with search on, Google’s AI Mode results, Claude through its API with web search | Two honest readings of “ChatGPT” disagree when the surface differs |
| Country and date | Where the question was asked from, and the week | Answers differ by country, and medians are taken week by week |
| Sources cited | Every address, or “none” | “None” is missing for a source question, never a zero |
| Tracked source present | Yes or no, only for answers that cited something | This is the zero that counts |
| Brand named | Yes or no, for every answer collected | Being named uses every answer, cited or not |
| Run status | Collected, failed or refused | A failed run is missing data |
The median is the last setting. Google AI Mode’s Reddit share ranged seven points over four weeks with nothing changing on our side, so one week alone could have read anywhere from 30% to 38%. The median of four takes the middle.
How many questions a reading needs before it holds is the other half of the instrument, and a smaller set than ours needs more weeks before a move clears its band.
A team without a panel can run all of this in a spreadsheet, one row per answer, filled by hand on the same weekday. That is how an AI visibility baseline for ChatGPT and Google AI Mode is built, with Claude added as a third set of rows.
An Engine’s Number Still Carries Its Question Mix
Read by engine, ChatGPT cites Reddit in 12% of its cited answers. Read again by the questions it was asked, most of that 12% belongs to one group. 221 of the 236 ChatGPT answers that cited Reddit came from the 75 questions about Reddit marketing itself.
On our other 425 questions, ChatGPT cited Reddit in 15 of 1,661 cited answers, under 1%. Google AI Mode reads Reddit across the board: it cites Reddit in 38% of its answers to questions about generative engine optimization, where ChatGPT does in 1%, and in 63% when the question is about Reddit.
Claude cited Reddit on none of its questions, including the eight about Reddit marketing, where ChatGPT cited Reddit in 24 of 32 answers to the same questions. On Claude’s 50 questions, which the other two engines answered as well, ChatGPT cited Reddit in 12.2% of its cited answers and Google AI Mode in 30.3%.
The zero belongs to the engine, and Claude’s smaller sample does not explain it. So the unit of the reading is the engine and the question group together. A ChatGPT Reddit share of 12% in our category mostly counts how many of our questions were about Reddit, and a question set leaning on one topic tilts every engine number built on it.
Each Engine Reads a Different Web, and That Decides the Work
Once each engine is read by question group, the useful part of an answer is what it cites. We sorted every source the three engines cited in our category over the four weeks into three kinds: Reddit, LinkedIn and YouTube; a company’s own front page; and “best of” list articles.
| Engine | Reddit, LinkedIn or YouTube | A company’s own front page | A “best of” list article (at least) |
|---|---|---|---|
| Google AI Mode | 58.2% | 13.6% | 83.8% |
| ChatGPT | 12.2% | 82.4% | 64.7% |
| Claude | 0% | 1.5% | 98.5% |
Each cell is the share of that engine’s cited answers with at least one page of that kind. A list article here is a page whose address names a best or top list, or a list of agencies, companies or firms. That test caught lists in 24 of 25 checks and misses some, so the column is a floor.
Across the rows, each engine has its own web. Google AI Mode reads the community platforms in more than half its answers, and LinkedIn alone turns up in 31.2% of them, against 2 of ChatGPT’s 1,956. ChatGPT reads the companies themselves, with a firm’s own front page among the sources in 82.4% of its cited answers.
Claude reads lists, and Wikipedia, which appears in 14.3% of its cited answers and almost nowhere else. It does not read Reddit at all. When we asked Claude’s web search to search reddit.com on October 28, it returned an error saying the domain is not accessible to its user agent.
Which kind of page reaches which engine
List articles are the one kind of page all three engines read. Our GEO work runs on the shorthand GEO = UGC + Mentions, which stands for user-generated content, what people say about a brand in public, plus what other sites write about it. This reading says which half reaches which engine:
Empact Partners gets partners into the list articles that already rank and already get cited, a method we call Existing Article Outreach, and into the Reddit threads where their buyers ask for recommendations. The per-engine reading decides how a quarter splits between the two.
What a practitioner on Reddit says about working from the citations
“whats worked for me so far is reverse engineering the citations, so looking at what pages each engine cites for my prompts and then getting placements on those sites / listicles.” — u/Biffxs, r/TechSEO, Oct 2026
Start With the Engine Your Buyers Ask and Your Work Can Reach
The lowest number on a per-engine report pulls the eye, and in our category it is Claude’s zero for Reddit. Aiming Reddit work at it would spend a quarter on an engine that cited Reddit in none of 196 answers and cannot open the site.
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The first engine comes from three inputs instead:
Run on our own category, the inputs give a ruling. The first work is getting named in the “best of” list articles, because they are the one source all three engines read, and because being on them goes with being named.
The evidence is an association, since lists may favor vendors the engines already liked. In a September test on the B2B software category our panel measures, ChatGPT named a vendor in 52.6% of the answers when a cited roundup listed it and 11.2% when none did. Claude ran 46.6% against 10.7%.
Reddit work comes second for us, because in our category it reaches Google AI Mode, and ChatGPT only when the question is about Reddit.
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What our panel cannot settle is which engine our own buyers use most, so it cannot rank Google AI Mode against ChatGPT for us. That answer comes from the pipeline, and it sets how much of the Reddit work is worth doing.
The ruling can be tested, and it should be. Split your questions into the ones whose cited lists now name you and the ones whose lists do not, then read each engine on both halves. If naming rises on the first half and holds on the second, the work moved it. If Claude stays flat while the other two rise, the ruling was wrong about Claude.
Report the Engines Side by Side, Even When the Board Wants One Number
The board will ask for one number anyway. Give it the range with each engine named: in our category, Reddit appears in none of Claude’s cited answers, 12% of ChatGPT’s and about 36% of Google AI Mode’s. That sentence is barely longer than the blended one, and it says where to work.
Each engine then gets its own row, and the row carries its decision. For our own category the report reads like this:
| Engine | Reddit share, median of four weeks | What it reads most | Work first |
|---|---|---|---|
| Google AI Mode | 35.7% | List articles, Reddit and LinkedIn | List articles, then Reddit threads where buyers ask |
| ChatGPT | 11.9%, about 1% outside Reddit questions | Firms’ own front pages, list articles | List articles, then a front page that says plainly what we do |
| Claude | 0% | List articles, Wikipedia | List articles only |
Empact Partners runs this same reading for partners. Every partnership opens with an audit and a roadmap, and on the GEO side this reading starts them, because it says which list articles and communities get the first quarter’s work. A named senior consultant runs it and joins your calls.
The Empact Panel then asks each partner’s buyer questions across ChatGPT, Google AI Mode and Claude every week, and the standing reports read each engine on its own, every recommendation with a first step. GEO takes months to show momentum, and the per-engine reading shows which engine moved first while the blend is still flat.
If the number on your slide blends engines that read different webs and you need to decide where next quarter’s GEO work goes, book a call with me. We’ll decide together whether we are the people to run it.
