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Brand Visibility
Large Language Models like ChatGPT and Gemini are becoming the first layer of brand discovery. Meikai tracks how your brand is mentioned, described, and recommended, and how that compares to your competitors.
37%
of search now starts with AI
2–3
brands recommended per AI answer
6+
LLMs monitored continuously
Understand how often your brand appears in AI responses across key topics and prompts. If your brand isn't mentioned in the first AI answer, you're invisible in the AI era.
Meikai runs structured prompt sets across leading LLMs, captures brand mentions, rankings, and citations, and benchmarks performance over time.
AI doesn't just mention brands; it frames them. Narrative positioning influences trust, authority, and purchase intent.
We compare your brand against key competitors across attributes and product categories, analysing how leading LLMs describe, rank, and recommend brands.
NikeLLMs often recommend only 2–3 brands. If you're not in that set, you're excluded from the most influential moment in the customer journey.
We analyse competitive visibility trends, presence distribution, share of voice by topic, and competitor-exclusive citations.
Nike
Adidas
New Balance
ASICS
Under ArmourA useful first audit answers three questions: whether your brand appears at all in the answers your buyers get, how often it appears relative to named competitors, and whether what is said about it is accurate. Manual spot checks cannot answer any of those reliably, because a single run of a prompt tells you nothing about how the model behaves across repeated runs.
The audit runs a standardised prompt set across the models your market actually uses, repeats it, and reports presence, position and share of voice against the competitors you name. Comparing two or three rivals on an identical prompt set is more informative than a score of your own, because it shows which competitor holds the position you want and which sources put them there.
Accuracy matters as much as presence. A model that recommends you while misdescribing a product does more damage than one that omits you, so the audit records how your brand is characterised, not only how often it appears.
Run it again a week later. A vendor who cannot explain the movement is itself the finding.
The spread column is the reason for repeated runs. A single Gemini query could have returned any answer within 11 points of this one.
Leadership does not need prompt logs. Four figures carry most of the meaning: share of voice against competitors, how often you appear in the top few recommendations and not merely mentioned, how your brand is framed, and which domains the models cite when they describe you.
Report those on the same prompt set each month so the comparison is real. Month-on-month movement on a stable set is a signal you can act on, whereas movement on a prompt set that changed between reports tells you very little. Where a change was deliberate, show the before and after, and be explicit about which part is measured and which part is inference.
Built for a standing monthly slot, not a deck assembled once and never repeated.
Share of voice
vs three named competitors
Leading recommendations
named in the first three, not just mentioned
Framing
answers describing the brand favourably
Citation concentration
share of answers resting on one domain
Four figures, one page, no prompt logs. Movement only means something because the prompt set was the same in both months.
Meikai runs structured prompt sets across major LLMs and measures brand mentions, ranking position, recommendation frequency, and citation sources.
It measures how often your brand appears in AI-generated answers compared to competitors across relevant prompts.
Yes. Meikai analyses sentiment and narrative framing inside LLM responses, so you understand not just whether you appear, but how you are described.
Yes. In many high-intent queries, LLMs recommend 2–3 brands. Visibility inside that set is the new competitive frontier.
A standardised prompt set run repeatedly across the models your market uses, reporting presence, position, share of voice against named competitors, and how accurately your brand is described.
By running an identical prompt set for you and the competitors you name, then comparing recommendation frequency, position and the sources cited for each. The comparison only means something if everyone is given the same prompts.
Share of voice against competitors, how often you appear in the leading recommendations and not only mentioned, how your brand is framed, and which domains are cited. Report them on a stable prompt set so month-on-month movement means something.
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