Do AI engines cite you?
More and more buyers ask ChatGPT, Claude or Perplexity first. This is the method to measure whether they cite a company correctly (30 fixed prompts, 5 engines, 3 languages), run on Slash first, with no numbers until the first run completes.
AI engines already speak about us to people who don't know us. If they're going to talk about Slash, we want to measure it: an open methodology, prompts and runs.
AI engines are a channel
And like every channel, it's either measured or ignored.
More buyers now ask an AI engine before they visit a website. If those engines can't retrieve primary facts about Slash (legal identity, headquarters, services, clients), they speak about us on hearsay.
That's why we publish verifiable facts on our company facts page and will measure whether they land.
30 prompts, 5 engines, 3 languages
No numbers until the first run completes.
The LLM Visibility Score is made of 30 fixed prompts about the company and its category, run on 5 engines in 3 languages (Spanish, English and French). Each run records the answer, the mentions and the citations.
We publish the methodology before the results: the study can be replicated and compared quarter over quarter.
Mention, citation, correctness
Three levels of landing.
An engine can ignore us, mention us on hearsay, or cite primary facts correctly. The score tells the three apart: a mention with wrong facts is worse than none.
The goal: every mention anchored to verifiable primary facts, not rumor.
What this study is not
Stated before publishing.
It isn't a paid advertising index or a game to trick the engines: it's an honest measurement with stated limitations (fixed sample, public engines and repeatable runs).
It follows the rules of the Slash AI Lab: methodology before numbers, limitations stated up front.
Key takeaways
- AI engines are already a channel: measured or ignored.
- 30 fixed prompts, 5 engines and 3 languages.
- A hearsay mention is not a correct citation of primary facts.
- The methodology is published before the results.
- Repeatable runs, comparable quarter over quarter.
Sources
Editorial note: this analysis reflects the public information available on the review date. Models, prices and rules change fast; every third-party figure links to its source, and our opinions are labeled as such. Spotted an error? Write to contact@slash-digital.io.
The questions we hear often
What is the LLM Visibility Score?
A measurement, with a public methodology, of whether AI engines know and correctly cite a company's primary facts.
Is the study finished?
No numbers are published until the first run completes; the methodology is already open.
Why does it matter?
Because buyers ask AI engines first; without primary facts, the engines answer on hearsay.
Can it be applied to other companies?
Yes, the method is replicable. Write to us for your company's version.
How often is it repeated?
Every quarter, or sooner when something relevant changes.
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