Understanding Scores
How Vantage calculates AI readiness scores — dimensions, weights, tiers, and what they mean for your store
Last updated: 18th February 2026
Understanding Scores
Every product in your catalog receives an AI Readiness Score from 0 to 100. Here's how it works, what it measures, and what the numbers mean.
How scores are calculated
Vantage scores are rule-based and deterministic. There are no AI or LLM calls involved in scoring — every check follows clear, documented rules. The same product data always produces the same score.
Your composite score is a weighted average of 9 individual dimension scores. Each dimension evaluates a specific aspect of your product data and produces its own 0-100 score.
Score tiers
| Tier | Score | Color | What it means |
|---|---|---|---|
| Excellent | 90-100 | Green | Your product data is comprehensive, well-structured, and highly visible to AI agents |
| Good | 70-89 | Blue | Strong foundation — a few targeted improvements will push you higher |
| Moderate | 50-69 | Yellow | Noticeable data gaps that may reduce how often AI agents recommend this product |
| Poor | 0-49 | Red | Critical issues — AI shopping agents are unlikely to surface this product |
What each tier means for AI visibility
AI shopping agents like ChatGPT, Google Gemini, and Microsoft Copilot rely on structured, complete product data to make recommendations. Products with rich titles, detailed descriptions, proper identifiers, and complete attributes are far more likely to appear in AI-generated shopping suggestions.
A product in the Excellent tier has the data AI agents need to confidently recommend it. A product in the Poor tier is essentially invisible to these systems.
The 9 scoring dimensions
| Dimension | Weight | What it measures |
|---|---|---|
| Title Quality | 16.7% | Length, brand inclusion, specificity, formatting |
| Description Quality | 16.7% | Word count, factual content, structure, attribute density |
| Attribute Completeness | 16.7% | Category-specific attributes filled in |
| Product Identifiers | 13.3% | SKUs, GTINs/UPCs, manufacturer part numbers |
| Category & Taxonomy | 11.1% | Category assignment, taxonomy depth, consistency |
| Image Quality | 8.9% | Image count, alt text, variety |
| Variant Structure | 5.6% | Option naming, SKU consistency, pricing |
| Pricing & Availability | 5.6% | Price presence, compare-at pricing, inventory status |
| Policy & Compliance | 5.6% | Return policy, shipping info, warranty, compliance |
The top three dimensions — Title Quality, Description Quality, and Attribute Completeness — account for 50.1% of your total score. Start there for maximum impact.
When do scores change?
Scores only update when your product data changes and Vantage re-syncs your catalog. Editing a product in Shopify doesn't instantly change its Vantage score — you need to trigger a sync (or wait for the next automatic sync) to see the updated numbers.
Your score is entirely in your control. Every improvement you make to product data directly increases your AI readiness. There's no algorithm to game — just make your data better.
Next steps
- Scoring Dimensions Overview — Deep dive into each dimension
- Improving Scores — Prioritized optimization playbook