The Proof Standard for AI Visibility Claims
One of the 10 Dubai agencies we measured shows any AI-answer evidence on its service page. The category has no proof standard, so here is one: six requirements, all cheap to meet, and a worked example carrying our own failing score.
By Firoz Azees · Published · 5 min read
A category without a proof standard runs on assertion. We measured 10 Dubai agencies across 8 buyer questions and found exactly 1 showing any measured AI-answer evidence on its own service page. The other 9 sell AI visibility on claims. This page proposes the standard, which costs nothing to meet and is the reason we can publish our own failures.
The short version
Six requirements make an AI-visibility claim checkable: a date, a named engine and locale, the exact questions asked, the full saved answers, named and cited counted separately, and the vendor's own score included. Every one is free. Across the 31 GEO products and 10 agencies we track, the category norm is a blended share-of-answer figure with none of the six, which is why a 300% improvement claim carries no information: a 300% lift on 1 mention is 4 mentions.
The six requirements
1. A date
Generated answers are assembled at query time from a changing pool. An undated claim is not a measurement. Our own readings carry three dates: 21 July, 7 September and 12 September 2026.
2. A named engine and locale
Answers differ by engine and by where you ask from. "AI search" is not a measurable object; Google AI Mode from a UAE browser is.
3. The exact questions
Publish them so the buyer can retype them. A question set held back as proprietary is protecting the claim, not the method.
4. The full saved answers
Screenshots crop. A stored answer with its source list is the artefact that survives disagreement.
5. Named and cited counted separately
AI Search Visibility is not one number.
Named and cited are different outcomes with different causes. Collapsing them hides the most useful diagnostic a buyer has, as our own panel showed when one agency was cited in 50% of answers while named in 12.5%.
6. The vendor's own score
A referee who hides their score is not a referee.
The standard as a table
| Requirement | Cost to meet | Met by the category |
|---|---|---|
| Date on every reading | Zero | Rare |
| Named engine and locale | Zero | Rare |
| Published question set | Zero | Almost never |
| Stored full answers | Near zero | Almost never |
| Named vs cited split | Zero | Almost never |
| Vendor's own score | Zero, and uncomfortable | 1 of 10 shows any evidence |
A worked example, carrying our own failure
Here is the standard applied to us. Engine: Google AI Mode. Locale: UAE. Dates: 21 July, 7 September, 12 September 2026. Questions: the 8 buyer questions listed in full on our Dubai agency measurement. Result: Ivanooo named 0%, cited 0%, on all three dates. Wider panel: Share of Recommendation 0.0 across 62 queries (16-18 September 2026), with 59 of 62 returning us absent, 2 citing us and 1 naming us.
That is a failing score published to the same standard we would use for a win. As Firoz Azees puts it: "a measured zero is a work order; a flattering estimate is a story you tell yourself."
How to apply it in a sales conversation
- Send the six requirements before the call. Vendors who can meet them will; the rest will reframe.
- Ask for two dated readings, not one. In a category where leaders turned over 100% inside 52 days, a single date proves nothing.
- Ask which questions they expect to lose. A practitioner can name them. A salesperson cannot.
- Ask for their own score. The answer tells you more than the deck.
- Keep your own baseline. The conversation changes when you hold the measurement rather than receiving it.
Questions
Is this standard too demanding for a small agency?
What about proprietary methodology?
Do the software tools meet it?
Why does the vendor's own score matter to me?
Does meeting the standard guarantee results?
How does this relate to the research?
What is the fastest way to start?
About the author
Firoz Azees, founder of Ivanooo. Fifteen years running growth for companies in Dubai, Singapore, London and Silicon Valley. Runs a 62-question AI answer panel and publishes what it measures.
First published 22 Sept 2026.
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