What you can check about us
Why this page exists
You are being asked to pay for a number about your own brand, produced by a company you have probably never heard of, measuring engines you cannot see inside. That is a lot of trust to ask for.
So this page states exactly what we measure, what the score does and does not mean, and where our own method is weak. The last part is the one competitors leave out — and it is the reason to believe the rest.
What a scan actually does
We generate 25 real buying questions for your category and market — the questions someone asks when they are ready to spend money, not brand-awareness questions. Each one is put to 5 answer engines:
- ChatGPT
- Claude
- Gemini
- Perplexity
- Lenscore Retrieval Engine — ours, not a third-party assistant: a self-hosted retrieval stack
Four of those are third-party assistants you can go and use yourself. The fifth is our own retrieval engine, and we say so here rather than letting five names in a row imply five outside products. It is included because retrieval — what gets pulled up in response to a question — is a real part of how you get recommended, and because we can inspect it in a way we cannot inspect the other four.
Each question is asked once per engine — 5 passes per question in total. We then record, verbatim, whether the engine named you, who it named instead and which sources it drew on.
The report shows you those answers. Not a summary of them — the actual text, and screenshots of what we saw.
The limits of what we measure
Every measurement method has edges. Here are ours, stated plainly, because you will find them eventually and it is better that you find them here.
One pass per engine is a sample, not a census
Each of the 25 questions is asked once per engine. That is enough to see whether an engine reaches for you at all, and not enough to measure a stable percentage. Read a 3-of-25 as "rarely", not as 12.0%.
AI answers move on their own
The same question can be answered differently next week without you or your rivals changing anything, because the engines change. A re-scan measures the engines on that day, which is why a single audit is a starting position rather than a permanent verdict.
We audit the questions buyers ask, not every question
The 25 questions are generated for your category and market. They are the commercial ones — what someone asks when they are ready to buy. A brand can be well known and still score low here, because being famous and being recommended at the point of purchase are different things.
Same formula, sampled answers
The score formula never changes: the same answers always produce the same number. The answers it reads are one day's sample from engines that change on their own. So two scans a week apart can differ, and the difference is the engines moving, not the method.
The category behind your questions may be read by a model
When our rules cannot place what you sell, a model reads your site text and picks one label from a fixed list. The report says so where it happens, and the confidence is capped at medium. A re-scan can resolve it differently; telling us your category makes it a declared input instead.
When we could not check something, we say so
A check that fails to run is recorded as not observed, and appears that way in the report. It is never rounded down to a zero, because a zero is a finding and a failure to look is not.
The rules we won't break to sell you something
- Deterministic scoring: the same inputs always produce the same score. No black box, no mood.
- We label estimates as estimates. A live AI measurement is never dressed up from a guess.
- Every question is unbranded. We ask what a buyer asks — "best X for Y" — never "is <your brand> good?". Being named is earned in the answer, not planted in the question.
- No guaranteed rankings or revenue. We report your current standing, not a promise.
- No fabricated testimonials or reviews, ever. The sample report is our proof, not invented quotes.
- No fake countdowns or pressure. The only urgency is real: AI answers change as you and rivals publish.
When we get it wrong
We do get things wrong. The audit infers your category and market from your website, and inference can miss — a streetwear brand was once read as skincare because one product name contained the letters "lip". When that happens the whole report is wrong, because every question was asked about the wrong market.
If your report describes a business that is not yours, tell us at [email protected] and we will re-run it corrected, or refund you. That is not a goodwill gesture — a report about the wrong category is not the product you bought. See the refund policy.
What leaves our servers and to whom
A scan sends your brand name, category and market inside a buying question to each AI engine we measure (ChatGPT, Claude, Gemini, Perplexity; Google AI Mode on paid tiers) and to the search index behind the Lenscore Retrieval Engine. Nothing else about you is sent: no email, no report, no other brand's data. Each provider's own privacy terms apply to that question.
The audit trail behind every number
Every report keeps the raw engine answers it was scored from, stamped with the methodology version that scored them. A number on a report can always be traced to the answer that produced it and a re-scan is only compared with an earlier one when both carry the same version. The docs list every version in force; the changelogrecords when one changed.
Your data
We crawl your public website and public social profiles — the same pages any visitor can open. We store the report, the evidence screenshots behind it and your email if you gave one. We do not sell it and we do not use one brand's report to inform another's.
Full detail in the privacy policy.
Who we are
LensCore is a product of Outkast Printwears LLP (LLPIN ACN-7933, GSTIN 33AAJFO5819N1ZS), a registered company in Chennai, India. Not a pseudonym, not an offshore shell — a small team, with a company number you can look up.
Reach a person at [email protected], or see contact. See also how it works for the method in more depth and the sample report for the real output.