Three approaches, not one category: checking by hand, scripting the APIs, and running a monitoring platform. What each one actually tells you, and the point at which paying for the next one starts to make sense.
Before comparing tools it is worth being precise about the thing being measured, because most disappointment with this category comes from measuring the wrong one.
Asking ChatGPT “what do you know about [your brand]?” tests whether the model has information about you. It almost always does, and the answer is reassuring and nearly useless. The question that matters is whether you are named when a buyer asks something that does not contain your name — “best tools for X”, “alternatives to [competitor]”, “how do I solve Y”. The gap between those two is usually the entire finding.
Any tool worth using measures unprompted presence across repeated runs. A tool that only confirms the model has heard of you is answering the easy question.
Open the engines, ask your buyers’ questions, write down what came back. It costs nothing, needs no procurement, and it is the only method that shows you exactly what a customer sees, on the real consumer surface, including the formatting and the links.
Its limit is arithmetic rather than effort. The same question asked twice does not reliably produce the same answer, so a single check cannot distinguish “we are absent” from “we were absent that time”. To get a rate rather than an anecdote you need the same prompts run repeatedly over weeks, and that is where doing it by hand quietly stops happening.
The engines expose APIs, and a modest script can run a prompt set against several of them, store the responses, and count mentions. This gives you volume and repeatability for the price of some engineering time.
The catch is that the API is not the same surface as the product your buyers use. It may perform different retrieval, apply different system instructions, and return different citations — so a brand that looks present in the API can be absent in the consumer app, and the reverse. You are also building the analysis layer yourself: mention detection, competitor extraction, citation parsing, and the historical baseline that makes any of it a trend.
Platforms in this category run a fixed prompt set against multiple engines on a schedule, analyse every response for brand mentions, competitor mentions and cited sources, and accumulate the results into a trend. InfluenceLayer is one of them, so treat this section as interested rather than neutral.
What you are buying is not the ability to ask an engine a question — you already have that for free. You are buying consistency, the competitor baseline, and the history that lets you say whether something changed. If you do not yet need to answer “is this getting better or worse, and against whom?”, you probably do not need one of these yet.
Four things separate a tool that produces a number from one that produces a decision.
Generated answers vary between runs. A tool that checks each prompt once reports noise with a confident interface around it. Ask how many times a prompt is run and over what window.
Appearing in forty percent of answers means nothing in isolation. If the strongest competitor appears in thirty, it is a lead; if they appear in eighty, it is an emergency. Visibility in a generated answer is relative by nature, so a tool that measures only you is measuring half the question.
Being named and being cited are different events with different fixes. An engine can recommend you without linking to you, or cite your documentation without naming you as an option. A tool that collapses both into one “visibility” score hides which problem you have.
The engines do not behave alike, and the gaps are large. In our own measurement of 1,690 answers, Perplexity included at least one source in 92.6% of them while ChatGPT did so in 27.8% — and the ordering held for every brand we could test. A tool that blends engines into a single score averages away the difference you would actually act on.
They are often bracketed together because buyers use both, but they fail differently. ChatGPT cites comparatively rarely, so a brand can be recommended there with no link to earn and no referral traffic to show for it. Gemini sits inside Google’s ecosystem and overlaps with AI Overviews, which means the sources that work for one often influence the other.
The practical consequence is that a result from one is not evidence about the other. Track them as two measurements that happen to be collected together.
InfluenceLayer runs that loop across ChatGPT, Gemini, Claude and Perplexity on a schedule, with competitors measured on the same prompts and cited sources recorded alongside mentions.
For the step-by-step method on a single engine, including which prompts to run and what to record: