Anyone can type a prompt into ChatGPT and screenshot the answer. The work that decides whether a report is worth paying for happens before that, in deciding which questions a real buyer in a real market would actually ask.
A question set is not a translation exercise. The same commercial need is expressed differently in Riyadh and in Cairo, differently by a procurement officer and by a technical lead, and differently again depending on whether the buyer already knows which companies exist. Getting this wrong produces a clean report that measures nothing.
Who initiates, who evaluates and who signs differ by industry. A distributor is chosen by a reseller's commercial team on credit terms and vendor coverage. A private label manufacturer is chosen by a foreign sourcing manager on certifications and minimum order quantities. A listed contractor is researched by analysts who care about backlog and governance. Each of those buyers types a different question, so each gets a different set.
A buyer who does not yet know which companies exist asks the assistant to produce candidates. A buyer who already has your name asks whether you are any good. Those two produce completely different results, and blending them into one average hides the finding. Most companies score well on the second and badly on the first, which is exactly the problem worth paying to fix.
Modern Standard Arabic for formal and Gulf corporate contexts, Gulf spoken phrasing, Egyptian spoken phrasing, and English. A procurement officer in Saudi Arabia does not type the same words as a factory manager in the Delta. Where a sector uses English technical vocabulary inside an Arabic sentence, which is common in technology, the question is written the way it is really typed rather than the way a purist would write it.
Draft questions get trial runs first. Any question that returns a definition rather than a list of companies is cut, because it measures nothing commercial. Any question that returns the same three companies every time regardless of phrasing is kept, because it is the one that decides deals.
Once a set is agreed the wording is frozen, because a re-test six weeks later is only meaningful if the question is identical. Changing a single word between the baseline and the re-test invalidates the comparison, which is a mistake that is easy to make and impossible to detect afterwards.
Sample from a technology sector set. The English question, then the same need as it was actually written in three Arabic registers.
| Register | Question |
|---|---|
| English | best IT distributors in Saudi Arabia |
| فصحى | أفضل شركات توزيع حلول تكنولوجيا المعلومات في السعودية |
| خليجي | مين أكبر موزع أجهزة وشبكات في الخليج؟ |
| مصري | إيه أحسن شركات توزيع IT بتدي تسهيلات ائتمانية في مصر؟ |
These assistants do not share a search index, and the difference between them is far larger than most people assume. Anthropic lists Brave Search as the provider behind Claude, and Brave runs its own index rather than reselling anyone else's results. OpenAI no longer uses Bing and now operates its own crawler, which means a site that blocks that crawler cannot appear in ChatGPT's answers regardless of how it ranks anywhere else.
Published research puts the overlap between Claude's citations and ChatGPT's at around eight percent. A company can be well represented inside one assistant and absent from another, so any report that covers all of them with a single number has not measured what it is charging for.
It also means the work is targetable. Getting into Brave's index is cheap and direct, and it affects Claude specifically. Correcting a vendor partner locator affects the assistants that lean on those pages. Knowing which lever moves which assistant is the difference between a plan and a wish.
ChatGPT reads OpenAI's own crawler index.
Claude reads Brave's independent index.
Gemini reads Google's.
Perplexity runs its own retrieval stack.
Copilot sits on Microsoft's.
Five indexes, five different sets of pages, five separate results for the same question.
The sample below is from an audit of a Gulf technology distributor. Six discovery questions were used, meaning questions from a buyer who does not yet know which companies exist. Each question was asked twenty times in Arabic and twenty times in English, spread across ChatGPT, Gemini and Perplexity, every run from a fresh session with no account signed in. The figures are illustrative.
Reading the bars. Each question was asked twenty times in each language, and each asking produced one answer. The bar shows how many of those twenty answers named the company, so 3 of 20 means the company was named in three answers and absent from the other seventeen.
Across all six discovery questions, 120 runs in each language. Shown as the share of runs that named each company, so the two languages can be compared directly.
| Company | Named, English | Named, Arabic | Change |
|---|---|---|---|
| Distributor A | 80% | 73% | ▼7 points |
| Distributor B | 68% | 65% | ▼3 points |
| Distributor C | 60% | 52% | ▼8 points |
| The client | 58% | 17% | ▼41 points |
Every competitor in the test loses a few points between languages, which is what the thinness of Arabic material on the web would predict. The client loses forty one. That difference is the finding, and it matters because a problem affecting the whole category cannot be fixed by one company while a problem specific to one company can.
A shortlist holds four names. In English the client is fourth. In Arabic it is sixth.
One further question was added outside the six, asked twenty times in each language.
| Answer returned | Runs | Share |
|---|---|---|
| Named the previous chief executive | 23 of 40 | 57% |
| Named the current chief executive | 11 of 40 | 28% |
| Declined or hedged between the two | 6 of 40 | 15% |
The cause was a single page. The company's own About page still named the outgoing chief executive while its investor announcement named the incoming one, and a company's own domain carries heavy weight when an assistant is asked to state a fact about that company. Two hours of work moved this to above eighty five percent within a month.
Ordered by how much each one moves the numbers rather than by effort.
| The work | What it changes | Effect |
|---|---|---|
| Arabic pages on your own domain | Creates material of your own for an Arabic search to retrieve, written to answer the questions buyers actually ask rather than to describe the company | Highest |
| Technical legibility | Crawler directives, server rendered text, sitemaps. A page that never enters an index cannot appear in an answer no matter how good it is | High, low cost |
| One canonical name per script | Decides whether years of accumulated mentions count towards one company or scatter across eight, with every profile linked so the names resolve to a single entity | High |
| Directory and data broker corrections | Fixes the sources that are cheapest for an assistant to trust and most often wrong, filed and then verified rather than assumed | Medium |
| Structured data | States your facts in a form that needs no interpretation, which is where invented details come from | Medium |
| Specifics worth quoting | Named clients, named projects, real figures, published in both languages. Specific material gets cited and generic capability statements do not | Medium |
Corrections do not appear in answers the day they are published. A search engine has to find the amended page, index it and start returning it before an assistant can read it, which takes somewhere between two and eight weeks depending on how often your site is crawled. The first re-measurement is scheduled accordingly.
A re-test carries an obvious objection, which is whether the second reading is contaminated by the first. It is handled three ways.
Every run happens in a fresh session with no account signed in, so nothing is being personalised around a history of asking about one company. The same questions run in parallel against two or three named competitors who had no work done, which turns a single reading into a comparison. And the wording, the number of runs and the assistants stay identical between the baseline and the re-test.
If your appearance rate rises while the control companies hold steady, the movement came from the work. If everyone in the test rises together, something changed inside the assistant rather than on your properties, and the report says so instead of claiming the credit.
Every figure in a report has been counted. Nothing is estimated, inferred or presented from a single screenshot.
Each report states what was measured, what was inferred, on which dates, and under which session conditions.
Where a claim rests on reasoning rather than counting, the report says which one it is.
Three Arabic questions for your category, counted, with the competitors that appeared in your place. No charge and no call.