Read the methodology
Methodology
How the AI Visibility Score is calculated: dimensions, weights and measurement rules. The formula lives in a single file (apps/web/src/lib/score.ts) and this page is generated from it.
Mention rate
Share of queries where the brand name or domain appears in the answer.
20%weight
Recommendation rate
Share of queries where the brand is explicitly suggested as an option (a mere mention does not count).
25%weight
Position
Rank among recommended businesses: 1st → 1.0, 2nd → 0.7, 3rd → 0.5, later → 0.3, absent → 0.
10%weight
Citation strength
Share of queries where the brand's domain is cited as a source.
10%weight
Entity completeness
7 Knowledge Graph signals: brand, ≥3 services, ≥1 person, ≥1 location, contact, ≥5 FAQs, ≥3 schema types.
15%weight
Local visibility
Mention rate on local-intent (district/city) queries.
10%weight
Competitive position
Your share of voice / the top share.
10%weight
Weights sum to 1.0. Total score = Σ (weight × dimension) × 100.
Per-engine score
Per engine: mention 40% · recommendation 40% · position 20%.
Measurement rules
- 01The query set is deterministic: the same brand, sector and language produce the same questions; the full set is 1,240 questions, run up to the plan limit.
- 02Every engine's raw answer is stored as-is; never deleted or overwritten. Judging (mentioned/recommended/position) uses an LLM; without a key a rule-based match is used and the record is marked 'heuristic'.
- 03Crawled page content is untrusted input; no instruction inside it is ever executed.
- 04No data means no score; an empty state is shown. No rate is ever estimated.
- 05Finding impact points are rule-based estimates (P1: 5–7, P2: 3–4, P3: 2–3) validated through experiments.