Understanding Scores
How Vantage calculates AI readiness scores — dimensions, weights, tiers, and what they mean for your store
Last updated: 8th September 2026
Understanding Scores
Products audited within your plan limit receive a 0–100 AI Readiness Score. It measures product-data signals in Vantage, not external search placement or the probability of an AI recommendation.
Calculation
There are 12 baseline dimensions, using rule-based checks of synced data. Title Quality, Description Quality and Attribute Completeness each contribute 15% of the baseline, totaling 45%. Reviews & Trust Signals is an additional, conditional dimension; missing reviews do not add a zero-score dimension. Processed signals may originate from the separate review-intelligence AI workflow.
The Scoring Dimensions overview lists all current weights and the normalized reviews calculation. Baseline image-count/alt-text checks are separate from visual image analysis.
Score tiers
| Tier | Score | Meaning within Vantage |
|---|---|---|
| Excellent | 90–100 | Strong coverage of the measured product-data checks |
| Good | 70–89 | A solid baseline with remaining gaps |
| Moderate | 50–69 | Several measured gaps to inspect |
| Poor | 0–49 | Substantial missing or weak data signals |
These tiers do not prove external visibility, recommendations or sales. Check each dimension and the actual source facts; a higher score is not a substitute for accurate product information.
Updating results
Inspect the product's most recent audit. After source changes, use the app's sync/audit controls and wait for completion before comparing results. Changes do not necessarily improve every dimension, and a previous score is historical until the product is audited again.
Start with missing factual information rather than adding unsupported claims to improve a score. See Improving Scores.