Feedmind
How it’s scored →
FeedmindAI readiness scorecard
chocoladebox.nl · 13-page sample
B
75/100
Feedmind Score

The Feedmind Score measures how readable your catalog is to an AI assistant.

Dimension scores
Dim 01 · ACCESSIBILITY
Agent accessibility100
Assistant crawlers reach and read your pages without JavaScript.
Dim 02 · STRUCTURED DATA
Structured data0
No schema.org Product markup found on the page.
Dim 03 · ANSWERABILITY
Content answerability100
Descriptions carry the concrete facts buyers actually ask about.
Dim 04 · LANGUAGE PARITY
Multi-language parity100
Every locale reaches the same depth and is exposed via hreflang.
Top 2 issues to fix first
01
No schema.org Product markup found on the page.
Add Product JSON-LD with name, description, brand, image and an Offer (price, currency, availability).
high
02
No GTIN (global product identifier) found on the page.
Map GTIN/EAN from your PIM into the Product markup so assistants can match your item to known products.
high
How it’s scored · FM-score methodology v1.0Full methodology →
Dimension weights
Agent accessibility25%
Structured data25%
Content answerability30%
Multi-language parity20%
Grade bands
A85 — 100
B65 — 84
C50 — 64
D35 — 49
Fbelow 35
Bands split in thirds for +/−.
Connect your Shopify store — full-catalog scan + fixesOr book 15 min →
chocoladebox.nl · scanned 01 AUG 2026 · FM-score methodology v1.0Feedmind Score · How it’s scored →
Scores reflect a 13-product sample · a connected store is scored across the full catalog
++Step 02 · locked
Step 02 · AI simulation
See the buyer questions AI assistants can’t answer about chocoladebox.nl
We run six real purchase questions against your product pages and show exactly where the assistant gives up. Your score is already yours — this part takes an email.
One email, one scorecard. No sequence, no reselling.
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