Peec AI Visibility Metrics Review

What AI visibility metrics measure. When a user types a question into ChatGPT, Perplexity, or Gemini, the model assembles a response from its training data, retrieval results, or both. Your brand either appears in that response or it does not. AI visibility metrics track the rate at which your brand (or a competitor) appears across a sampled set of prompts on a given engine. The core signal is share of voice: the fraction of sampled prompts in which your brand is mentioned. Secondary signals include whether your domain is cited as a source, what sentiment the model expresses, and which competitor brands co-appear.

How Peec collects the data. Peec.ai runs a pre-defined prompt set against each tracked engine on a recurring schedule, records the full response text, and classifies each response for brand mentions, source URLs, and sentiment (positive / neutral / negative). The metrics are then aggregated into a dashboard that shows share of voice, citation frequency (the ratio of responses that explicitly cite your domain versus those that merely mention your brand name), and sentiment distribution. Regional variants of the same prompt can be run to produce country-level breakdowns, though the number of supported countries is tied to the plan tier. Peec does not publish the full prompt-sampling design, sample sizes per engine, or reproducibility methodology, so absolute point-in-time figures should be read as directional.

Engine coverage and what it means in practice. Peec covers six engines: ChatGPT, Perplexity, Gemini, Microsoft Copilot, Google AI Mode, and Google AI Overviews. The inclusion of Google AI Mode and AI Overviews is a meaningful differentiator for brands whose customers use Google Search, since those surfaces now deliver AI-generated answers at the top of results. Claude (Anthropic) is not currently covered, which is a gap for brands tracked by enterprise buyers who use Claude-powered tools. The base plan covers three engines; additional engines are available at extra cost per Peec's published tier structure.

Sentiment analysis depth. Peec classifies each response as positive, neutral, or negative for your brand. Three-class sentiment is sufficient for a top-level health check, but falls short for teams that need to understand which topics or product claims drive negative responses. A 5-class model or topic-tagged sentiment can reveal, for example, that a brand scores well overall but consistently receives negative framing when AI engines address pricing. Peec does not offer topic-level sentiment in its current product; that distinction matters when building content-optimization strategies.

Evaluating what matters. When comparing AI visibility platforms, the questions that matter most are: (1) Does the prompt set reflect the queries your actual customers ask, or is it generic? (2) Is the sampling design disclosed enough to reproduce or challenge the results? (3) Are trend lines reliable even if absolute figures are noisy? Peec's Looker Studio connector and recurring cadence make trend tracking straightforward. The undisclosed sampling design means precision-critical use cases should be validated independently.