Metrisque Launches the First Way to Measure AI Visibility That Gives the Same Answer

Pre-registered study finds 92% of AI product recommendations land where the instrument predicted before the models were asked

SAN FRANCISCO, CA, UNITED STATES, September 6, 2026 /EINPresswire.com/ -- Metrisque today launched a measurement instrument that tells companies where AI models place their brand, products, and services, and gives the same answer every time it is queried in an LLM.

Ask an AI assistant the same shopping question twice and it usually names different products making AI visibility almost impossible to measure. LLMs change the reading on every run, so a company can never tell whether something they changed actually worked, or whether the model simply answered differently that day.

Metrisque measures a different thing. Instead of counting how often a brand appears in AI answers, it measures how closely a company’s own words match what buyers are asking. The reading is consistent, so a change can be made, measured, and proven.

Proving it out
In a pre-registered study published with a permanent citation, DOI 10.5281/zenodo.21417361, roughly eleven hundred real recommendations from two leading AI models were tested against predictions made before the models were asked anything. Ninety-two percent landed where the prediction said they would.

"We also published the prediction we got wrong. A company that only shows you its wins hasn't shown you anything."
— Lian Pham, Co-founder, Metrisque

Metrisque measures what matters
Metrisque reads six things a company cannot see any other way.
Brand Recall shows what AI models already believe about a company with nothing open in front of them, whether they know it exists, and whether what they recall is true. This is the belief that forms before anyone asks a question, and it decides the answers a company never sees.
Category Fit shows how AI models categorize a company and its individual products. Knowing the brand is not the same as filing each product correctly. One beauty brand's makeup set was filed by an AI model as a bug collecting kit, because the words on the page a collection, gotta catch them all sounded like catching creatures. The company was understood perfectly. The product was on the wrong shelf entirely. Metrisque shows the shelf the models actually put each one on.

Buyer Match shows whether a company's words match the questions buyers ask AI assistants. There are twenty ways to ask for the same thing, and chasing each phrase is endless. People invent new ones daily. Metrisque measures the one thing that holds still: whether a company's words are reachable from what the buyer meant, however they typed it.

AI Recommendations shows who gets named when a buyer asks, and where a company sits against them. Before it answers, a model weighs a longer list of names it might use. A company can be on that list and still not be named. Metrisque reads both: whether a company is in the running at all, and how it ranks against everyone else being weighed.

Competitor's Citations shows which websites each AI model actually reads when it answers a buyer's question, and which of them already name the company. The models do not read the same web of forty-six sources cited on one question, only one was cited by all three, and thirty-nine were cited by a single model. Being covered in the right place for one model does nothing for the others.

Question Finder shows which buyer question is worth fighting for. The same question can look settled to one instrument and wide open to another what a model recalls from memory, what it finds when it searches, and what it weighs before answering can each name a different leader. Metrisque reads all three and shows whether a question is open, contested, or already owned, before a company spends anything trying to win it.

Why the measurements are unstable elsewhere.
Metrisque’s own research shows how much it moves between runs.
Ask three leading AI models the same buyer question and they read almost entirely different websites. Of forty-six sources cited on one question, only one was cited by all three. Thirty-nine were cited by a single model. Being visible to one AI says almost nothing about the others.

Ask the same model the same question days apart and roughly sixty percent of the websites it reads have changed.

The market.
“Every channel that mattered got placed and had a measurement layer before it got a budget. Search and social also had one. AI answers didn’t have one given the speed at which things moved and the money is already moving. Companies can’t tell whether it worked, and agencies are carrying the risk of that answer.”
— Lian Pham, Co-founder

From a pilot customer.
“Two of the three AIs didn’t know who we were, and the one that did had us categorized as something we don’t sell. That was not a marketing problem; it was a much earlier problem, and we couldn’t see it until we measured it. We changed one page, measured it again, and the number moved.”
— Rosmon Sidhik, Co-founder, The F* Word, pilot customer

Availability.
Metrisque is available at metrisque.com and companies can calibrate their visibility posture or that of their products and services.

Nitin Kumar
Metrisque, by Telesuite
+1 408-915-8627
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