Track AI visibility (ChatGPT, Perplexity, Gemini)

Classic SEO measures whether you're in Google's top 10. AI visibility measures whether you appear in answers from ChatGPT, Perplexity, Gemini and Claude at all — and if so, how. This guide walks you through the full tracking workflow.

What is AI visibility?

LLMs generate answers from their own training, from web searches or from retrieval-augmented sources. When a user asks "What is the best vegan protein source?", the model lists vendors, brands and URLs. Whoever gets named there wins traffic, trust and market share. Whoever doesn't is invisible — no matter how good the Google position is.

Rankion automatically and periodically asks your brand-relevant questions to several models and logs: are you mentioned? In what context? Which sources does the model cite?

Create a tracking project

In the menu: AI Visibility → New tracking. The wizard asks you:

1. Name — e.g. "Vegan protein DACH"
2. Mode — domain tracking (one brand/domain) or keyword tracking (topic, all relevant players)
3. Domain or keywords — what is being tracked

Via the API:

curl -X POST https://rankion.ai/api/v1/tracking-projects \
  -H "Authorization: Bearer YOUR_TOKEN" \
  -d '{"name": "Vegan protein DACH", "mode": "domain", "domain": "mydomain.com"}'

If your team already has an SEO project, the still-empty AI-Visibility page offers it to you directly. Clicking it starts the wizard with the name and domain already filled in, instead of typing everything from scratch. The "New tracking" path stays available alongside it.

Define brand aliases

Brands have spelling variants. "MyDomain", "My Domain Inc.", "MD24" — all the same company. Maintain aliases on the project so mentions are attributed correctly. Without aliases you miss hits.

Pick platforms

Pick which models are queried. Typical selection:

{
  "llm_platforms": ["chatgpt", "perplexity", "gemini", "claude"]
}

Each platform costs credits per run. You can also pick just one if your market is clear (e.g. only Perplexity for tech B2B).

Set the frequency

Five frequencies are available:

Before you save, the picker already shows what a frequency costs — for example "Weekly — up to approx. 200 credits/month". This per-frequency preview appears in three places: the setup wizard, an AI-visibility project's settings (project view), and the project's SEO settings. The Prompt monitoring page has no per-frequency picker; it shows the total for the current prompt selection at the set frequency, and a help icon explains the math. In the project view's settings and on the monitoring page, the calculation always uses the currently set value, even unsaved.

1 credit is counted per response received: a response is generated per active prompt × platform × search mode (with/without web search); Google AI Mode runs with search only, Grok without. With Reality Check on, both search modes count plus 15 credits per run; with Google AI Overviews on, add 1 credit per active keyword. The monthly total multiplies credits per run by the highest number of runs possible in a calendar month (table above) — hence "up to": many months have fewer runs, and only responses actually received are charged. Your plan limits how many prompts and platforms are included per run; the preview already accounts for that limit.

Example: 20 prompts × 2 platforms × 1 search mode = 40 credits per run — weekly up to approx. 200 credits/month, daily up to approx. 1,240.

Via the API: GET /api/v1/tracking-projects/{id}/prompts/cost-preview returns monthly_credits, credits_per_run and credit_breakdown; prompt_ids[]=… restricts to a subset.

For most brands, weekly is enough; fast-moving markets (news, crypto) benefit from daily.

Start a run + wait

The first run is automatically scheduled when you create the project. To trigger one manually:

curl -X POST https://rankion.ai/api/v1/tracking-projects/{id}/run \
  -H "Authorization: Bearer YOUR_TOKEN"

Response: HTTP 202 with a job ID. Duration depends on number of questions × platforms — usually 2–10 minutes. The UI polls automatically and shows live progress.

Interpret the score

After the run you see:

• Visibility score (0–100) — how often you were mentioned, in what position of the answer, with what sentiment.
• Reality check / AVI — compares the model's knowledge with the real top-10 SERPs. Big gap = LLM remembers you, Google doesn't (or vice versa).
• Platform breakdown — where you're strong, where you're weak.
• Trends — historical development per run.

Analyze citations

The exciting part is the cited sources. Models often link to specific URLs in their answers. Rankion collects them, aggregates per domain and shows you:

• Which URLs are cited most often?
• Which competitors are at the top?
• Which of your own pieces of content aren't picked up at all?

That's your content roadmap: topics where you're missing are your next articles — see Your first article with the AI Content Editor.

Read on

In-depth knowledge of setup, limits, the data model and every endpoint lives in AI Visibility Tracking and the API reference. Questions on credits & quotas are answered in Credits & Rate Limits.