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The Rankahead dashboard is your command center for AI visibility. It aggregates data from every prompt run across ChatGPT, Perplexity, Google AI Overviews, and Anthropic, and surfaces the metrics that matter most — so you can see at a glance how often your brand appears in AI-generated answers and how that changes over time.
The dashboard displays data only after you have run at least one prompt. If you are seeing an empty state, head to Prompts to create and run your first tracking query.

Key performance indicators

The top of the dashboard shows four primary KPIs. Each one is calculated across all active prompts and all connected LLM providers.

Visibility score

A composite score from 0 to 100 that reflects how prominently your brand appears across AI answers. It weighs mention rate, position in responses, and sentiment. A score above 70 is considered strong; below 30 indicates significant gaps to address.

Mention rate

The percentage of prompt runs where your brand was mentioned at least once in an AI response. A run that queries four LLM providers counts as four opportunities; mention rate is calculated across all of them.

Share of voice

Your brand’s proportion of total brand mentions in AI responses compared to tracked competitors. If your brand appears in 30 out of 100 total brand mentions, your share of voice is 30%. This metric is only meaningful when you are tracking at least one competitor domain.

Answer gaps

The number of topics or prompts where your brand is absent from AI answers entirely. This count links directly to the Gap Analysis page, where you can see which gaps are critical versus moderate and prioritize your response.

Per-LLM scores

Below the main KPIs, the dashboard breaks down your visibility score for each AI provider individually. This is useful because different models draw from different training data, retrieval systems, and citation preferences.
Shows your visibility score across prompts run against GPT-4 and GPT-4o. ChatGPT tends to rely on knowledge cut-off data and browsed sources when web browsing is enabled. A low score here often signals a need for stronger authoritative content and backlinks from sources the model trusts.

Trend charts

The main chart panel shows how each metric has moved over the selected time window. You can switch between:
  • 7 days — useful for spotting the immediate impact of a content publish or backlink campaign
  • 30 days — the default view; good for tracking month-over-month progress
  • 90 days — reveals longer-term trajectory and seasonal patterns
Each line on the chart corresponds to one LLM provider. Hovering over a data point shows the exact score for that provider on that date, plus the number of prompt runs that contributed to the calculation.
If you see a sudden drop on a specific LLM’s line, check whether any prompts failed to run on that date. Failed runs are excluded from score calculations, which can create artificial dips. You can review run statuses on the Prompts page.

How scores are calculated

Rankahead runs each of your prompts against every configured LLM provider on the schedule you define. After each run completes, the platform:
  1. Parses every LLM response for brand mentions, sentiment, and position.
  2. Computes a per-run score for your brand.
  3. Aggregates scores across all runs within the selected time window to produce the KPIs shown on the dashboard.
Scores update as soon as a run completes — there is no fixed reporting delay. Manual runs triggered from the Prompts page appear in the dashboard within seconds of completion.
Share of voice requires at least one competitor domain to be configured. You can add competitor domains under Settings → Domains. Without a competitor, only your brand’s raw mention rate and visibility score are shown.

Next steps

Create your first prompt

Define the queries that AI models answer and start tracking where your brand appears.

Identify answer gaps

See which topics your brand is missing from and get prioritized content recommendations.

Review citation sources

Understand which domains AI models trust and how to get your content cited more often.

Connect Google Search Console

Layer traditional search data on top of your AI visibility metrics.