Perception

Visibility tells you whether a model mentioned your brand. Perception tells you what it said about you. Open a client's AI Visibility project and pick Perception from the Brand group in the left-hand nav, and you get the words the models reach for when they describe each tracked brand — "responsive", "expensive", "white-label", "enterprise-focused" — with how often each one came up. Every tracked brand gets its own card: your client's brand first, then the competitors, ordered by how much evidence there is behind them. Switch the range at the top between the last 7, 30 or 90 days to see whether the language around a brand is shifting or holding steady. It's the view that answers the question a client actually asks after "are we visible?" — namely, "and how are we being described?"

Nothing new is being computed here and no second AI pass is run to produce it. When a scan reads an answer and records a brand mention, the extractor also pulls one to eight short descriptive terms for that brand, taken verbatim from the answer text, under an explicit instruction never to invent a term that isn't there. That has been happening on every scan for a long time — Perception is simply the page that finally shows it, which is why a project with scan history has something to read on day one rather than starting from zero. To be precise about what this is not: it isn't entity extraction, and the terms aren't resolved against any external database or knowledge base. They're descriptive words the model used, attached to a brand you already told us to track. If a model didn't describe a brand in a given answer, that mention simply carries no terms — nothing is filled in for it.

Each card lists that brand's top 20 terms, most frequent first, with a count and a share. Read the two sample sizes in the card header before the terms themselves: it shows how many mentions carried terms alongside the brand's total mentions, because those two numbers are usually different. The share is calculated against the mentions that actually carried terms — never against all mentions — since a mention with no descriptive terms could never have produced one, and using the bigger number would quietly overstate the evidence. The bars are scaled within each brand, against that brand's own top term. That's deliberate: a brand is read against itself, not against a competitor who happens to have five times the sample. Comparing raw bar widths across two cards would tell you more about mention volume than about language. And a brand with fewer than five attributed mentions is labelled "too few mentions to read as a pattern" — it's still shown, because hiding data is worse than qualifying it, but the label is there so nobody builds a strategy on three data points.

A term marked with a fingerprint icon is distinctive: across the whole project, in the range you're looking at, it was used for that brand and for no other tracked brand. That's where positioning becomes visible. "Fast" applied to everyone in the category is table stakes and tells you very little; "white-label" applied only to you is a position you own in the models' language — and a competitor's distinctive terms are the ground they own that you don't. Distinctiveness is relative to the brands in that project and to the selected range, so it will change as you add or remove competitors or move between 7, 30 and 90 days. Treat a distinctive term as a prompt for a question — is this how we want to be described, and is the competitor's distinctive term something we should be competing for? — rather than as a score.

Each card shows a sentiment score out of 100 next to the sample sizes. That score is the brand's overall tone across its mentions in the range. It is not attached to any individual term, and Perception will never show you a sentiment figure beside a term — because we don't measure that. The reason is worth stating plainly rather than burying: the descriptive terms and the sentiment score are recorded separately on each mention, with no link between them. So the honest reading is "the models described you as 'slow support', and your overall tone score is 45" — two facts, side by side. The reading we can't support is "your sentiment on support is 45", because nothing in the data connects a specific term to a specific score. If you want to know why a tone score is what it is, the terms are strong evidence to read alongside it; they are not a per-aspect breakdown of it.

The same data goes into reports. Add a Table widget, choose the AI Visibility source, and set its dimension to "Perception". The table has five columns — Brand, Term, Mentions, Share and "Only this brand" — so a client sees the language, its frequency, its share of that brand's attributed mentions, and whether it's used for them alone. As on the page, the client's brand sorts first, and there's deliberately no sentiment column. One difference from the in-app page: in a report, a brand with fewer than five attributed mentions is dropped from the table rather than labelled. On screen you can read a caveat next to a thin sample; in a client report a footnote nobody reads is worse than simply not showing a row that can't carry its own weight.

If Perception shows nothing, it usually isn't a fault. The most common cause is a project imported from Peec AI: Peec-imported mentions are stored without descriptive terms, so an imported project has nothing for this page to show, no matter how much visibility data it carries. That's a limitation of what the import supplies, not a bug — and it resolves for that client the moment you add native prompts and run CrunchJunkie's own scans, which populate terms from then on. Two other honest reasons for a thin or empty page: a brand-new project simply hasn't scanned enough yet, and a shorter range may fall outside your scan history — try 90 days before concluding there's nothing there. And within a range, some mentions genuinely carry no terms because the model named the brand without describing it, which is exactly why the card reports attributed mentions separately from total mentions.