Agentic Commerce Readiness Audit
Score how ready your product catalog is for AI shopping agents like ChatGPT and Perplexity. Free audit across 9 dimensions in ~60 seconds.
Most catalogs were built for humans browsing in a browser, not for machines parsing schema. AI shopping agents — ChatGPT, Perplexity, Gemini, Claude — read catalogs the way a careful, literal-minded buyer would. They check for structured data, identifiers, spec tables, and clean metadata. Products they can't parse get skipped. Products with ambiguous facts get misrepresented in the answer. This audit quantifies that gap and gives engineering a ranked list of what to fix first.
Why agentic commerce changes the brief
When a shopper asks an agent "which 12-foot kayak under $800?", the agent isn't returning a search results page. It's synthesizing a recommendation from catalogs it can read and trust. The winning product has valid Schema.org Product markup, populated identifiers (GTIN, MPN, brand), an extractable spec table, and copy that doesn't contradict the spec. Everything else is invisible.
The shift matters because the buyer doesn't see the catalogs that lost. There's no SERP to scroll. Either you're in the answer, or you weren't considered.
What the audit measures
Nine weighted dimensions of product data quality, rolled into a single 0–100 PDQ score:
- ACR · 25% — can an agent reliably extract title, identifiers, and spec?
- Completeness · 25% — are the attributes Google Merchant expects present?
- Conformity · 10% — do values follow expected formats (units, IP ratings, identifiers)?
- Consistency · 10% — are units and vocabulary consistent across the catalog?
- Uniqueness · 10% — are SKUs, titles, identifiers actually unique?
- Validity · 10% — are critical and recommended fields present and non-empty?
- Referential Integrity · 5% — do images and links resolve cleanly?
- Accessibility · 5% — is the spec extractable as key:value pairs?
- Accuracy · penalty — do title and description claims match the spec table?
Full methodology: the PDQ framework.
What you get
A 0–100 PDQ score banded as Excellent, Good, Fair, or Poor — the headline number you'll quote to leadership and re-audit against next sprint. RAG status per dimension so the team can see at a glance where the bleeding is. A ranked gap list ordered by composite impact (weight × coverage × confidence) so engineering ships in priority order, no triage meeting needed. PDF and Excel exports for the exec summary and the per-SKU punch list.
Who runs this audit
Digital and e-commerce leads whose CEO just asked "are we ready for ChatGPT shopping?" — this is the answer, with a number you can quote. Heads of catalog and PIM who own product data quality and need to know what an agent actually sees on the live storefront, not what the PIM thinks it's shipping. Agencies running paid audits across a portfolio of client catalogs.
Related
Free. No signup needed. 9 dimensions audited. Ranked fix list when you're done.
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