The pet nutrition industry has a critical urgency on ingredient composition: it's an EU legal obligation for pet food. AI agents seek to verify meat percentage, ingredient origin, and absence of artificial additives. Veterinary validation is a strong trust signal, and species/age filter is essential for precise recommendations. The industry is competitive.
01 · The checklist
The data engines look for on a pet product page
Excerpts from the sector checklist, ranked by weight: what blocks a recommendation when missing, then what reinforces trust. The full audit monitors 100+ signals.
Required content
Industry-specific audit zones
Composition ingrédients (% viande, origine, sans additifs artificiels) visible sur PDP
Validation vétérinaire ou recommandation expert visible sur PDP
Filtre espèce (chien/chat) et tranche d'âge accessible PDP
Trust signals
Expected UX patterns
Daily ration calculator based on pet weight, breed, and activity level
Filter by species (dog, cat, small pet) and age range (puppy, adult, senior)
02 · Industry benchmarks
Reference values for a pet page
Internal reference valuesPer-sector reference targets defined by Verity Score, not measured on a sample of stores.
03 · Required schema.org
The markup engines can read
Copy the structure and adapt the values to your pages.
"additionalProperty": [{"@type": "PropertyValue", "name": "Animal Type", "value": "Dog"}]05 · Frequent questions
Your questions about AI engines
Why is ingredient composition a critical urgency?
It's an EU legal obligation for pet food. AI agents filter by meat percentage, origin, and additive-free claims. Without this data in schema.org, your product is excluded from recommendations.
Does veterinary validation have an impact?
Yes. The audit evaluates visible veterinary recommendation. It is the sector's reference proof of expertise: marked up, it is readable and verifiable by AI.
How do I structure animal type?
Add additionalProperty name='Animal Type' value='Dog' in schema.org Product. AI agents filter by species for queries like 'senior dog food'.
Is the ration calculator detected?
Yes. UX patterns ration_calculator and species_filter are detected and valued. They generate personalized recommendations extractable by AI.
Are industry benchmarks competitive?
Yes. 7 structured fields, 9 AI-extractable claims, 2 trust signals. Leaders (Royal Canin, Hill's) perfectly structure composition and species data.
Is AAFCO compliance detected as an AI signal in US/Canada markets?
Yes. AI agents use AAFCO (Association of American Feed Control Officials) compliance as a nutritional quality signal in North American markets. Expose 'AAFCO compliant' and 'complete and balanced' formulation statements in an additionalProperty in schema.org to maximize cross-market visibility.
How do I pass species and life stage through the ChatGPT product feed?
The OpenAI spec (Agentic Commerce developer docs, consulted August 13, 2026) has no dedicated species field : use variant_dict, the per-variant attribute map, to carry species, life stage (puppy, adult, senior), breed size and bag format. Add unit_pricing_measure and base_measure to show price per kilo, the only way to be compared fairly between a 3 kg and a 12 kg bag, and q_and_a for diet transition questions.
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