Proposals for generative engine optimization, or GEO, read alike. Each promises to get your brand named when a buyer asks ChatGPT or Google AI Mode what to buy, each lists content, mentions and community work, and one usually carries a screenshot of a visibility score that jumped in a week.
The tactics will not separate those proposals, because capable teams run much the same ones. The measurement will. This page shows how to test it in the pitch meeting with four requests, what a good answer to each sounds like, and why the screenshot proves less than it looks.
The consultancy writing this is Empact Partners, and since 2020 our go-to-market consultants have sat inside the marketing teams of software companies we partner with. In our generative engine optimization work we get partners into the threads, reviews and list articles AI engines read, then check every week whether the answers name them.
The Questions Somebody Picks Decide the Visibility Number
The checking runs on a panel we built. For more than a dozen partners and for our own category, it puts a fixed set of buyer questions to ChatGPT and Google AI Mode every week, with Claude on a sample. Our own set is 500 questions a marketing leader might ask when looking for outside help, all written on 4 October 2026.
Each question gets one answer per engine per week, and single answers move a lot. When an engine named a brand on one of our questions, it named that brand on the same question the following week 46.7% of the time on ChatGPT and 36.8% on Google AI Mode.
Across all 500 questions those swings mostly cancel out. For the five brands each engine names most often in our category, the full-set number moved a median of 2.4 points a week on ChatGPT and 2.2 on Google AI Mode. The trouble starts when somebody counts some of the questions instead of all of them. Here is one brand’s week, counted three ways.
The brand Google AI Mode names most often in our category appeared in 24.6% of its answers on 23 October and in 24.6% a week later, across the full set.
Count only the 377 questions where it had been missing, and the same week looks like a launch, from 0% to 17%. Count only the 123 where it had been named, and it looks like a collapse to 48%. Both subsets were chosen by looking at the earlier week, which is the whole trick.
Questions where a brand was missing will show a rise on the next read, and questions where it appeared will show a fall. The smaller the set, the bigger the swing, and a jump from nothing to a third of answers in a week says more about who chose the questions than about the work that went into them.
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So ask of any GEO proposal whether its measurement could have shown a loss as well as a gain. Four requests settle that, in this order, because each one assumes the one before it.
| Ask for | A good answer | Walk away when |
|---|---|---|
| Who wrote the questions, and when | Written before the work, from what you sell and who buys it, including questions you lose today, then frozen | The questions arrive with the first report, or carry your brand name |
| A dated baseline on each engine | A date, the number of answers behind it, a figure per engine, and anything that changed in how answers were collected | One blended score with no date and no count |
| One answer counted as a win | The answer itself, recommending you for the buyer’s situation and getting your product right | A list of twelve names, or the wrong price |
| The log of what shipped | Dated changes beside the weekly numbers, and the claim written as contribution | A claim that the work caused the rise |
Ask Who Wrote the Questions, and When
The first request is the question set itself, with the date it was written. A set written before the work can surprise everyone, including the people who wrote it. A set written afterwards tends to describe the places where the brand already shows up, whether or not anyone meant it to.
A set worth trusting shows all of these, and checking takes about an hour:
Empact Partners builds every partner’s panel to that standard. We write the questions from the partner’s own site, its positioning and the situations its buyers face, and grade each one. A second pass then reads the whole set looking for flaws, and a person checks everything it flags.
Once the partner approves the set, the file is locked. Change one character and collection stops until somebody approves it again, which is the least glamorous rule in the whole build and the one that makes week six comparable with week one.
Ask for a Dated Baseline on Each Engine
The second request is the starting reading: its date, how many answers stand behind each number, and a separate figure for each engine. The engines read different parts of the web: over five weeks on our 500 questions, ChatGPT named 159 of the 165 brands we track, Google AI Mode 129. A blended score can rise while the engine your buyers use falls.
Ask too whether it was taken before the work or partway through. For partnerships that began before our panel existed, ours were taken partway, and we read them as a starting line, never a before picture. If nobody on your shortlist can show you either, a baseline you take yourself with ten questions and a sheet will do for the next meeting.
Then ask what changed in the measurement, which moves numbers as easily as the work does. Between our own first and second weekly reads we changed the vendor that collects ChatGPT’s answers, and on the same 500 questions the share carrying a source went from 91.4% to 100%. Only the instrument had changed.
Open One Answer the Report Counts as a Win
The third request takes five minutes. Pick one answer the report counted in your favor and read it with the consultancy in the room. Is your product recommended for the buyer’s situation, or is it one name in a list of twelve? Is what the answer says about you true: the price, the features, who you sell to?
A visibility score cannot see that difference, ours included. Our panel counts a brand as recommended when the answer names it, and it records the order brands appear in, how many share the answer and the tone. It does not check whether the answer is right, which is why the answers matter more than the score.
What practitioners on Reddit say about counting a win
“Recommendation share: who is actually recommended when the prompt has commercial intent, not just who gets mentioned somewhere in the answer.” — u/ErikCodes, r/bigseo, Aug 2026
“One gap i see: sentiment/accuracy, not just framing but whether the answer is factually correct about you (wrong pricing, outdated features, misattributed claims). That’s a different failure mode than positioning and needs its own fix.” — u/HumanBehavi0ur, r/bigseo, Aug 2026
Both want a win you could show your own sales team without wincing.
A Rise on a Fixed Set Is a Contribution, Not a Cause
The fourth request is the log of what shipped, dated, beside the weekly numbers. A rise on a frozen set is real movement, and it still does not say what caused it: your own team published, a competitor stumbled, an engine changed what it reads. The log shows whether the rise followed the work or came before it.
Ask for the claim in writing, with its limit inside it. Our panel reports caption every lift the same way, as an association and not proof of cause, because that is all a weekly read of public answers can carry. A consultancy claiming cause for a single week is selling you the screenshot.
What a practitioner on Reddit says about proving it
“Don’t claim cause if you cant prove it. Frame it as contribution and show a timeline with GEO changes next to lift in AI visibility then say other SEO and PR work likely helped too.” — u/socleads, r/SEO, Sep 2026
The timeline is the part most proposals leave out, because it is the part that could show the work did nothing.
The First Report Should Tell Your Team Where to Start
The honest objection to all of this is cost. Measuring properly means weeks of reading before any visible gain, and a team under pressure would rather spend those weeks on the work.
The real waste is a report that changes nothing the team does next. After the first measured week your team should hold a work order: the engine that leaves you out of your buyers’ answers, the sources it reads for those questions, and the one place the first month’s work starts.
Our own panel reports are built for that. When one recommends a move, it names the evidence and the first step. A move to get the partner into the cited Reddit threads that leave them out names the thread to start with, and a move to pitch the cited list articles that omit them names the site.
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Put the timeline on the table in the same meeting. In our GEO partnerships momentum shows in three to six months, and significant visibility takes a year or more. A proposal promising a step change in a week promises more than its own measurement could confirm.
Judge Us by the Same Four Tests
We sell this work, so weigh the advice accordingly and put the four requests to us. A GEO partnership with Empact Partners opens with an audit of where AI engines name you and where they name somebody else. Then we write your question set, you approve it, we lock it, and the panel reads it every week.
A named senior consultant owns the partnership and works in your channels. The GEO work usually runs beside content marketing and Search Everywhere Optimization, our search work across AI engines and communities as well as Google, because the pages the engines quote have to exist first.
The first months mostly show which questions you lose and why. When momentum comes, it shows on the frozen set, where nobody chose the questions after the fact. If you are comparing GEO proposals and none of them can show you how the numbers were made, book a call with me, and let’s see whether we are the right consultancy for the work.
