AI visibility is best treated as repeated measurement, not a one-off vanity check. The core loop is simple: define prompts, query selected engines on a cadence, store the response and sources, then aggregate stable metrics.
Three practical metrics
- Mention rate: how often the answer names your brand.
- Citation rate: how often the answer cites your domain.
- Share of voice: your appearances relative to a fixed competitor set.
Sampling design matters
Generated answers vary. Freeze a useful prompt set, keep geography consistent, sample repeatedly, and interpret trends rather than treating one run as definitive. Store raw responses so you can recompute metrics when your scoring model changes.
Pipeline shape
Prompt set → engine fan-out → normalized JSON → raw storage → domain/entity classification → metric aggregation → dashboard or alerting.
Build the data layer, not another scraper
Cloro is designed to return answer text plus parsed sources across major AI-search surfaces, which can simplify the collection layer.
Try CloroAffiliate link.