Being Recommended by AI-Powered LLMs
Discover what large language models think about your business or offerings
A pre-registered study revealed that 92% of AI-generated product suggestions appeared exactly where the instrument forecasted prior to any model queries
“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/ — Today, Metrisque unveiled a measurement tool that reveals how AI models categorize a company’s brand, products, and services, and produces an identical result every time it is polled within an LLM.
Posing the same shopping question to an AI assistant on two separate occasions typically yields different product names, rendering AI visibility nearly impossible to track. Because LLMs alter their output with each query, companies cannot determine whether a change they made actually had an effect or if the model simply responded differently that day.
Metrisque measures something different. Rather than tallying how frequently a brand appears in AI responses, it evaluates how closely a company’s own terminology aligns with what buyers are searching for. The reading stays stable, enabling teams to implement a change, measure it, and validate the outcome.
Validating the approach
In a pre-registered study that carries a permanent citation (DOI 10.5281/zenodo.21417361), roughly 1,100 actual recommendations from two prominent AI models were compared against forecasts made before any model was queried. Ninety-two percent matched the predicted outcome.
"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 captures six insights that are otherwise invisible to any company.
Brand Recall reveals what AI models already think about a company without any external input — whether they are aware it exists and whether that knowledge is accurate. This belief forms before any question is asked and determines the answers a company never sees.
Category Fit shows how AI models classify a company and its specific products. Knowing the brand is not the same as correctly categorizing each product. One beauty brand’s makeup set was labeled by an AI model as a bug-collecting kit because the wording on the page — “a collection, gotta catch them all” — suggested catching creatures. The company was understood perfectly, but the product was placed on the wrong shelf entirely. Metrisque reveals the actual shelf where each item resides.
Buyer Match determines whether a company’s phrasing matches the questions buyers pose to AI assistants. There are twenty ways to request the same thing, and chasing each variant is endless. People invent new phrases daily. Metrisque measures the one constant: whether a company’s words can be reached from the buyer’s intent, regardless of how they typed it.
AI Recommendations identifies which names appear when a buyer asks, and where a company ranks relative to others. Before answering, a model evaluates a longer list of potential names. A company can be on that list yet still not be mentioned. Metrisque reads both: whether a company is in consideration at all and how it compares to every other contender being weighed.
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 referenced 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 indicates which buyer question is worth competing for. The same question can appear settled to one instrument yet wide open to another — what a model recalls from memory, what it retrieves when searching, 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 measurements fluctuate elsewhere.
Metrisque’s own research demonstrates how much variation occurs between runs.
Ask three leading AI models the same buyer question and they consult almost entirely different websites. Of 46 sources cited for 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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