How we measure AI visibility
In one paragraph
ModernTechLap agrees a set of buyer questions with you, runs them against AI assistants under recorded conditions, and has a researcher read every answer. The report reproduces each prompt beside the answer it produced, states the conditions of the run, and says plainly where the evidence stops.
Most vendors in this category will tell you a number and not how they got it. We think that is backwards. A visibility score you cannot audit is an assertion, and the first question any serious buyer asks about one is the question the method has to answer. So here is the whole thing, including the parts that are weak.
How a report is produced
We agree the questions before we run them
The research starts from the questions your buyers actually ask, not from keywords. We draft a prompt set from what you sell, who you sell it to, and which vendors you come up against, then send it to you before anything is run. If a question is wrong, or the obvious one is missing, that is the cheapest possible moment to fix it.
Every prompt is recorded verbatim
The report lists each prompt exactly as it was submitted, alongside the answer it produced. Nothing in the findings is derived from a question you cannot read. If we paraphrase an answer, the quote is there underneath it.
The run conditions are stamped on the report
Which assistants were used, on what date, from what location, in what language, and whether the session carried any account history. These change the answers, so a report that omits them is not reproducible even in principle.
A person reads the answers
Whether a brand was mentioned is usually obvious. Whether it was recommended, dismissed in passing, or named only as an also-ran is a judgement, and a keyword match gets it wrong often enough to matter. That reading is done by a researcher, and the raw answer is in the report so you can disagree with it.
How the questions are chosen
Prompts are built from your own words: what you sell, who buys it, where, and against whom. They are grouped by buying intent, because a brand can be strong on problem-solving questions and absent from the recommendation questions that decide a shortlist, and an average across the two hides exactly the thing worth knowing.
- Recommendation
- Which companies would you recommend for X?
- Best-of
- What are the best X providers?
- Comparison
- How does A compare with B?
- Alternatives
- What are the alternatives to A?
- Commercial
- I need X. Which vendors should I evaluate?
- Problem-solving
- How do I fix X?
How many prompts a report covers depends on how many genuinely distinct buying questions your category has. Rather than commit to a number that would be arbitrary for one client and thin for another, we agree the set with you up front and print all of it in the report.
Competitors, entities and sources
Competitors are yours plus the ones that show up. You give us the list you compete with. We report on those, and separately on any company the assistants named that you did not mention, which is usually the more useful half.
Brand names are matched as written.If an answer says “Acme” and you trade as “Acme Corp.”, we record the difference rather than normalising it away. A model using a shortened or wrong form of your name is a finding about how it holds your entity, not noise to clean up.
Sources are recorded when the assistant gives them. We note which domains the answers leaned on and whether any of them are yours. When an assistant does not cite anything, the report says so instead of inferring.
What this method cannot tell you
Every measurement has an edge. These are ours, and they are here rather than in a footnote because a buyer who finds them later is entitled to wonder what else was filed under small print.
It is a snapshot, not a ranking
The same question can return a different shortlist tomorrow. A report describes what a set of assistants said on a specific day under stated conditions. Treat a single run as one sample, and a trend across repeated runs as the actual signal.
Absence in a tested prompt is not universal absence
If your brand does not appear for the twelve questions we ran, that is evidence about those twelve questions. It is not proof that no assistant ever names you. We write findings that way, and you should read them that way.
Personalization is not fully controllable
Assistants vary their answers by account history, region and rollout. We record the conditions we ran under and hold them steady between runs, which makes comparisons meaningful. It does not mean your buyer sees precisely what we saw.
We do not have the models' ranking logic
Nobody outside these companies does. We can show what changed and when, and which sources the answers leaned on. We cannot tell you the weighting that produced it, and any vendor who claims to is guessing.
Sources are what the assistant surfaced
When an assistant cites its sources we record them. When it does not, we cannot reconstruct what it drew on, and the report says so rather than filling the gap with an inference.
What we do not guarantee
We do not guarantee that any AI assistant will mention, recommend or cite your brand, and we will not sell you a package that implies otherwise. The models belong to other companies, their selection logic is not published, and their output changes between runs. What we sell is a measurement you can check and a prioritised list of changes that are worth making on their own merits.
Common questions
How does ModernTechLap measure AI visibility?
Is the AI visibility report automated?
Can I see the prompts you used?
Why does the same question give different answers?
See what the method produces
The sample report shows the output of everything on this page, on an illustrative company. Or send us your details and we will run it on yours.