When AI does the shopping, your product data does the selling.
Shopping journeys increasingly start — and end — inside an AI conversation. The products recommended are the ones whose data machines can read, parse, and verify. Commerce Readiness makes yours one of them.
Audit the machine's view of your catalog.
- 1
Crawl like a bot
We read your product pages exactly as AI crawlers do — no JavaScript execution, no assumptions — and record what survives.
- 2
Validate the structure
Product schema, pricing markup, availability, reviews, and identifiers are checked against what AI shopping surfaces require to tile a product.
- 3
Check the claims
What answers currently say about your products is compared to your source of truth — wrong prices, dead SKUs, and misattributed features get flagged.
- 4
Fix with playbooks
Every failed check maps to a drafted remediation: schema patches, static-rendered pricing, feed corrections — approval-gated like everything else.
A readiness score you can act on.
Readiness score per SKU
A 0–100 audit of every product page: schema, crawlability, attribute completeness, and claim accuracy.
Attribute accuracy watch
How answers describe your products versus your actual specs — drift gets flagged before it costs sales.
Schema & feed validation
Machine-checkable structured data, validated continuously — not once at launch.
JS-invisible detection
Prices and specs rendered client-side are invisible to most AI crawlers. We find every instance.
Category benchmarks
Your readiness versus rivals' product pages, so merchandising knows exactly where the bar is.
Shopping-prompt tracking
Product-intent prompts in your tracked set, measured daily like everything else in Answer Intelligence.
Foundation first, surface second.
AI shopping interfaces are changing quarterly. Chasing each one's quirks is a treadmill; the durable asset is product data that any machine can read and trust.
- Standards-based: schema.org and open feed formats, not one vendor's tile layout
- Every fix improves all surfaces at once — search, shopping, and answers
- As official commerce interfaces open up, your catalog is already fluent
The quiet advantage
Most brands discover their pricing is invisible to AI the day a customer quotes a competitor's number back to them. The fix is usually a one-line rendering change — the hard part is knowing. Commerce Readiness is the knowing, continuously.
Commerce Readiness FAQ
Is this only for e-commerce brands?
It's for anyone whose products or plans get compared in AI conversations — DTC catalogs, SaaS pricing pages, marketplaces. If a buyer can ask 'how much does it cost' or 'which one should I buy', readiness applies.
Which standards do you validate against?
schema.org Product, Offer, and AggregateRating markup, open product-feed formats, and crawlability requirements published by the major AI operators. Checks update as the standards do.
Can you see inside proprietary shopping interfaces?
We track shopping-intent prompts through the same compliant measurement as the rest of the platform, and we audit everything on your side of the equation. What we won't do is scrape consumer apps in violation of their terms — your data should never carry that liability.
How do fixes ship?
Through Playbooks: schema patches and content changes arrive as drafted, reviewable diffs for your team or agency to apply. Readiness re-scores automatically after deployment.
Find out what AI thinks your products cost.
Self-serve. Transparent pricing. No sales call required.