Being Selected by AI LLMs
Find out what LLMs are saying about your business or offerings
In a pre-registered trial, 92% of AI product suggestions matched the locations the tool forecast before the models were consulted
“Every AI already has a belief about your company and has filed your products on a shelf before anyone asks it anything, and it has always been invisible.
— Lian Pham, Co-founder, Metrisque”— Lian PhamSAN FRANCISCO, CA, UNITED STATES, September 6, 2026 /EINPresswire.com/ — Metrisque has unveiled a measurement tool that reveals where AI models position a company’s brand, products, and services, and delivers identical results each time an LLM is queried.
When an AI assistant is asked the same shopping question twice, it often offers different product names, which makes AI visibility nearly impossible to track. Because LLMs produce varying outputs with every run, a business can never determine if a change they made actually caused a difference or if the model just responded differently that day.
Metrisque focuses on a different metric. Rather than tallying how often a brand appears in AI answers, it gauges how closely a company’s own language aligns with what buyers are searching for. Since the reading stays steady, any adjustment can be implemented, measured, and verified.
Validating the approach
In a pre-registered study that received a permanent citation (DOI 10.5281/zenodo.21417361), approximately 1,100 actual recommendations from two leading AI models were checked against forecasts made before the models were ever asked. 92% landed exactly where the forecast indicated 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
What Metrisque measures that matters
Metrisque detects six factors that a company cannot see through any other method.
Brand Recall reveals what AI models already think about a company without any context loaded—whether they are aware of its existence and whether that awareness is accurate. This is the belief that forms before any query is made, and it determines the answers a company never sees.
Category Fit shows how AI models classify a company and its individual products. Knowing the brand is not the same as correctly categorizing each item. For instance, one beauty brand’s makeup set was labeled by an AI as a bug collecting kit because the wording on the page—a collection, gotta catch them all—sounded like catching insects. The company was understood perfectly. The product was placed on the wrong shelf entirely. Metrisque reveals which shelf the models actually assigned each item to.
Buyer Match determines whether a company’s phrasing matches the questions buyers pose to AI. There are countless ways to ask for the same thing, and chasing every variation is futile. People invent new ones daily. Metrisque measures the single stable element: whether a company’s language is reachable from what the buyer intended, regardless of how they typed it.
AI Recommendations identifies which companies are named when a buyer asks, and where a company stands relative to them. Before it responds, a model considers a longer list of possible names. A company can be on that list and still not get mentioned. Metrisque captures both: whether a company is in contention at all and how it ranks among all the other contenders being evaluated.
Competitor's Citations shows which websites each AI model actually consults when answering a buyer’s question, and which of those sites already mention the company. The models do not all read the same web—out of 46 sources cited for one question, only one was cited by all three, and 39 were cited by a single model. Being covered in the right place for one model does nothing for the others.
Question Finder reveals which buyer question is most worth pursuing. A single question might appear settled to one tool yet completely open to another—what a model recalls from memory, what it finds when it searches, and what it weighs before answering can each point to a different leader. Metrisque reads all three and shows whether a question is open, contested, or already owned, prior to a company spending anything to win it.
Why other measurements fluctuate.
Metrisque’s own research highlights how much variation exists between runs.
Ask three leading AI models the same buyer question and they consult almost entirely different websites. Out of 46 sources cited for one question, only one was cited by all three. 39 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 60% of the websites it reads have changed.
The market context.
“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 now accessible at metrisque.com, and companies can calibrate their own visibility posture or that of their products and services.
Nitin Kumar
Metrisque, by Telesuite
+1 408-915-8627
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