AI Discovery Score
The AI Discovery Score is a 0–100 composite built from deterministic observation data. It answers one question quickly: how discoverable is this domain across modern AI systems right now?
Formula
Score = weighted average of six components. Each component is normalized to 0–100, then multiplied by its weight. Recalculates after prompt runs, audit runs, integration syncs, and scheduled recalc jobs.
Percent of tracked answers where the domain is directly cited.
Source: visibility_observations
How often the brand is mentioned across the tracked AI answer set.
Source: visibility_observations
How much of the important page set can AI crawlers actually reach.
Source: bot_requests + page_snapshots + audit_issues
Positive, neutral, and negative portrayal in generated answers.
Source: visibility_observations
How many of the four tracked engines cite the domain.
Source: visibility_observations
Strength of the domains surrounding your citations and mentions.
Source: visibility_observations
Tiers
AI systems rarely surface or cite the domain.
The domain appears inconsistently and lacks repeatable coverage.
The domain is regularly cited and recognized in-category.
The domain is consistently surfaced across the tracked engines.
The domain is first-to-mind in the active prompt set.
Update cadence
- Prompt batches trigger a recalculation after new observations land.
- Audit runs trigger recalculation after new access blockers or page states are found.
- GA4 and GSC sync jobs can trigger recalculation when performance data changes.
- Score history is stored for long-term trend analysis.
Caveats
- The score is only as complete as the connected prompts, crawl data, and imports.
- Estimated public scores for unclaimed domains use corpus evidence and are lower-confidence.
- Methodology weights may evolve; changes are reflected in the changelog below.