Make your product catalog easier for AI systems to verify and cite.
Resolved identity, complete attributes and source-backed product claims give search and AI systems a cleaner factual layer to work from.
Answer engines weigh a product by whether it can be checked
Answer engines are becoming a real discovery channel, and they behave nothing like a search index — they weigh a product by whether they can verify it, not by how it is worded. A record with a missing spec, a duplicate SKU across two suppliers, or a category that does not hold up gets skipped in favor of a competitor whose data is cleaner, even when the underlying product is identical or worse. Claro resolves identity, fills and verifies attributes with provenance, and keeps classification confidence-scored, so an AI agent has something solid to point to.
Why AI assistants skip most product catalogs
Missing specifications mean an AI agent cannot verify a claim, so it will not make it.
Duplicate and fragmented records across suppliers confuse which listing is even the real product.
A wrong or borderline category means the product is being shown to the wrong audience, or not at all.
How to make a catalog AI-citable
Product identity across every supplier and source.
Attributes filled and checked, with provenance.
Classification confidence-scored against your taxonomy.
Continuous checks so the data does not decay.
Who this matters to first
What goes in, what comes back
Claro writes back through files and APIs rather than certified connectors, so this list is a guide, not a limit.
AI search optimization: common questions
Is this SEO, or something different?
It is adjacent. Classic SEO optimises how a page is worded and linked. This is about whether the underlying product data is complete, consistent and checkable enough to be quoted — often called generative engine optimisation or answer engine optimisation.
Can you guarantee our products get cited?
No, and be sceptical of anyone who does. Assistants do not publish their ranking, and we cannot see inside it. What we can do is remove the specific reasons a product gets skipped: missing specs, duplicate identity, a category that does not hold up, values with no source behind them.
What actually changes on our side?
Product identity is resolved so one product is one record, attributes are filled from cited sources, and classification is confidence-scored. The visible result is a catalog where a claim about a product can be checked against a document.
How would we measure it?
Start with what is measurable on your own data: attribute completeness by category, duplicate rate, share of values carrying a source. Assistant citations are worth tracking as a trend, but they are downstream of the data work, not a substitute for measuring it.
Does this replace our product content team?
No. It gives them a catalog where the factual layer is already verified, so their time goes into positioning and merchandising rather than chasing missing specs.
Related work
See it work on your own catalog.
Bring one supplier file and we'll run ai search optimization on your real data — matched, classified and reviewable.