How to check brand AI-search visibility in n8n

The shift you cannot see from your analytics

For two decades, "are we visible?" meant "do we rank on the first page?" That question is being quietly replaced. More and more buyers open an AI assistant and ask it directly: what is the best tool for this, what are the alternatives to that, which option should a team like mine pick? The assistant answers with a short list of names. If your brand is on that list, you are in the consideration set. If it is not, you never entered the race — and nothing in your normal analytics will tell you, because there was no click to measure.

This is the blind spot. Your traffic dashboards show visits that happened; they cannot show the recommendations that never mentioned you. A competitor can climb into every assistant's default answer while your numbers look stable, and you would not know until pipeline dried up. AI-search visibility is the metric that closes that gap, and like any metric that matters, it needs to be measured on a schedule, not checked once and forgotten.

What AI-search visibility actually measures

A useful visibility check answers a few concrete questions, not a vague "are we known":

• Are you recommended at all? Across the assistants buyers use, does your brand get named for the prompts that matter in your category?
• Where do you stand versus competitors? Share of voice — how often you appear relative to the others — is the competitive reality, because being mentioned fifth is very different from being mentioned first.
• Which prompts are you missing from? The specific buyer questions where competitors appear and you do not are your to-do list.

Measured together, these turn a fuzzy worry into a number you can watch move. And once it is a number, n8n can track it for you and tell you when it changes — which is exactly what the MentionFox AI Visibility Monitor node does.

The monitoring workflow in n8n

The pattern is a scheduled check with a memory:

1. A schedule trigger. Once a day is the right default. It runs while you sleep and reports before you start.
2. A visibility check on your domain. The node measures whether you are recommended, your share of voice against a competitor list you provide, and optionally the prompts you are missing from.
3. A comparison against the last run. A small code step stores the latest score and computes the change versus yesterday. This is what makes the workflow a monitor rather than a daily report you stop reading.
4. A conditional alert. An IF node forwards to Slack only when the score drops, when no assistant recommends you, or when a competitor overtakes you. On a flat or improving day, it stays quiet.

The "only alert on a drop" design is deliberate. A daily message that says "still fine" trains people to ignore it; a message that only arrives when something is wrong gets read. The first run has nothing to compare against, so it simply records a baseline and alerts begin from the second day.

From alert to fix

An alert that you have slipped is only useful if it points at a remedy. That is why the "missing prompts" view matters: it names the exact buyer questions where you are absent. Those become content and positioning work — making sure the answer to "best tool for X" has a clear, well-sourced reason to include you. You can extend the workflow to log every run to a sheet so you have a trend line, or to fetch the missing-prompts list on a drop and post it alongside the alert so the team has the to-do in hand. Because it is all in n8n, adding a branch is a node, not a project.

Cost and cadence

A visibility check is a paid operation because it runs a real measurement across assistants. The base check is inexpensive; adding a competitive share-of-voice comparison costs more, which is why you run it daily rather than hourly and only pull the deeper breakdowns when you need them. For most brands a single daily run with a competitor list is the right balance of insight and spend. If you manage several brands or clients, run one workflow each on a staggered schedule rather than one giant check.

Why a schedule beats a one-off check

It is tempting to run a visibility check once, see your standing, and move on. That snapshot is almost worthless on its own, because the number that matters is not where you are today — it is which direction you are moving. Visibility shifts as assistants update, as competitors publish, and as the questions buyers ask evolve. A single reading cannot tell you whether you are climbing or sliding; only a series can. That is the whole reason to put the check on a schedule and store each result: you are building a trend line, and the trend is the signal. A drop from a strong position is an early warning that something changed — a competitor's new content, a shift in how a category is described — and catching it within a day gives you time to respond before it shows up as lost pipeline. A one-off check is a photograph; a scheduled monitor is the security camera, and only one of those tells you when something is wrong.

Where this lands: the MentionFox n8n template Daily AI-Visibility Check → Score-Drop Alert is exactly this workflow, prebuilt — schedule, check, compare, alert. It runs the MentionFox AI Visibility Monitor on your FoxAPIs credits (key at foxapis.com), so you import it, set your domain and competitors, and the daily watch is live.

On credits: the MentionFox n8n node runs on FoxAPIs credits — priced at foxapis.com/pricing and separate from MentionFox subscription plans. Get your free API key at foxapis.com/trial.

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