Claro is the AI execution layer for product and supplier data.
Berlin, DE
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Same product. Different supplier name. Different unit. Different SKU.
Now decide if it is duplicate, variant or replacement.
getclaro.ai/catalog-matching…
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Mapping supplier_voltage → voltage is easy.
Mapping supplier item → exact existing product is where expensive mistakes happen.
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A missing attribute used to mean a worse product page. Soon it can mean an AI buyer never considers the product.
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Feeds are becoming inputs for machine-mediated consideration. Bad feed data can remove a product before a human ever sees it.
getclaro.ai/resources/tools/…
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AI discovery matters before autonomous purchasing does.
The machine doesn't need permission to buy your product to influence whether your product reaches the shortlist
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Give it a set of supplier/manufacturer URLs, ask for the same fields across all of them, and get one sourced table instead of 30 browser tabs.
getclaro.ai/ai-research-agen…
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Industrial AI doesn't only need more documents.
It needs documents connected to entities.
A technical manual becomes much more useful when every extracted fact knows which exact product/variant it belongs to.
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Before blaming the model, check the product record: identifiers, variants, prices, availability and decision-critical attributes still determine what machines can use.
getclaro.ai/resources/tools/…
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Most AI-commerce discussion focuses on checkout. The more immediate shift may be upstream: machine-mediated consideration.
Which products even make it into the answer?
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Supplier onboarding is not “upload file → import products.” It is reconciliation. The supplier describes their world. Your ERP/PIM describes yours. Someone has to resolve the two.
getclaro.ai/by-use-case/supp…
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A supplier sends 4,000 rows. AI reading the file is not the interesting problem.
The interesting problem is deciding: existing product / new product / variant / duplicate / replacement / invalid record
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The fastest way to find flaws in a scoring methodology is to run it on your own site.
Every embarrassing result is either a scoring bug or a product requirement.
getclaro.ai/tools/product-pa…
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SEO asks: can the page be found?
AI product readiness also asks: can the product be understood, compared and its claims verified?
Different optimization problem.
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The interesting thing about AI agents isn't that they can read data.
It's that they're turning data-quality mistakes into actions.
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AI search optimization is not only crawler access.
If identity, variants, specs and availability are unclear, the agent can read the page and still skip the product.
getclaro.ai/by-use-case/ai-s…
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We ran our own AI/agent-readiness audit.
getclaro.ai scored 53/100 - Grade D
Inconvenient, and therefore useful.
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