How Agent Search Visibility Works Cloud

Agent Search Visibility is calculated from five weighted components:

Score = Frequency  x 40%
      + Sentiment  x 25%
      + Position   x 15%
      + CrossEngine x 10%
      + Coverage    x 10%

AI engines are non-deterministic — the same query can produce different results each time. A single check can be misleading. To measure actual visibility, we run each query multiple times:

• 3 engines selected — 3 runs per engine (balances cost and accuracy)
• 1-4 engines selected — 5 runs per engine (more runs for higher confidence)
• All runs execute in parallel for speed

Frequency is then calculated as: mentions / total runs. If your brand appears in 4 out of 5 runs for a keyword, that is an 80% frequency component for that keyword on that engine.

This approach catches brands that appear intermittently versus those that are consistently recommended — a distinction that single-run checks miss entirely.

Not all queries are equal. A user asking “what is project management software?” is at a different stage than one asking “Asana vs Monday.com for remote teams.” We classify queries into four funnel stages:

Each keyword generates queries across all four stages. This gives you a full picture — from category awareness to purchase-intent queries.

Keywords are automatically classified into three types, because they measure different things:

• Generic (organic) — Category-level queries like “best CRM software”. These measure real organic visibility and are the primary scoring input.
• Branded — Queries that include your brand name. These always score ~100% and are excluded from the organic score to avoid inflation.
• Competitor — Queries about specific competitors. Tracked separately to measure your share of voice in competitor conversations.

The headline score uses only generic keywords. Branded and competitor scores are shown separately in the Analysis tab.

When your brand appears in an AI response, position matters. Being the first recommendation carries more weight than being listed fifth. Position score uses exponential decay:

Position Score = 100 x 0.9 ^ position

Position 0 (first mentioned): 100
Position 1 (second):           90
Position 2 (third):            81
Position 3 (fourth):           73
Position 4 (fifth):            66
Position 9 (tenth):            35

This models real user behavior — most people pay attention to the first few recommendations and skim the rest.

Each AI response is analyzed for sentiment toward your brand:

Sometimes AI engines cite your URL without mentioning your brand name. For example, a Perplexity response might include [example.com] as a source without ever writing “Example Brand.”

These ghost citations are tracked separately. They indicate that your content is being used as a source even when you are not explicitly recommended — a signal that improving your brand prominence could convert citations into mentions.

Being mentioned by one engine is good. Being mentioned by all five is a strong signal. The cross-engine component measures what percentage of monitored engines mention your brand for a given keyword.

If you monitor 3 engines and your brand appears on 2 of them, the cross-engine component for that keyword is 67%.