Our Scoring Methodology
How we calculate AI Citation Readiness
Built for how AI search actually works
AI search engines like ChatGPT, Perplexity and Gemini don't rank products the way Google does. They cite products.
When a shopper asks “what's the best carbon fibre snowboard for park riding under $500”, an AI engine scans billions of pages and selects the products it can cite with confidence. The products it cites are the ones with enough specific, verifiable information to support the recommendation.
We built our scoring system by analysing what makes a product citable — what information AI engines need to confidently recommend a specific product over a generic one.
The result is four scoring dimensions:
Visual richness — Can the AI describe what the product looks like from the image description alone?
Technical specificity — Does the product have verifiable facts like measurements, materials and certifications that support a confident citation?
Semantic context — Does the AI understand who this product is for and what it is used for?
Unique identity — Is this product distinct enough to be cited specifically, or does it blend into thousands of similar listings?
A product that scores well across all four dimensions is one that AI engines can recommend with confidence. That is what Citation Ready means.
How the score is calculated
Color, texture, shape and form factor visible in the product image.
AI systems build visual understanding from image descriptions. A product described as 'purple snowboard with hexagonal graphics and matte finish' is more citable than 'a snowboard'.
Our AI automatically captures visual details. Add texture and finish descriptions manually for maximum points.
Measurements, materials, certifications and technical specifications.
Technical specificity is the strongest signal for AI citation. When a shopper asks ChatGPT for '158cm carbon fibre snowboard', only products with those exact specs in their data will be cited.
Add dimensions, weight, material composition and any certifications. These must come from you — our AI cannot invent specs it cannot see.
Use case, application context and description length.
AI engines match products to queries based on semantic context. 'Designed for park and freeride conditions' tells AI exactly when to recommend your product.
Include who the product is for and what it's used for. Our AI captures use case from your product tags and description.
Non-generic language and product-specific details.
Generic descriptions like 'a great product' or 'high quality item' are invisible to AI. Unique, specific descriptions stand out.
Avoid generic openers. Our AI is prompted to never start with 'A photo of' or use generic language.
Grade thresholds
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