Velou builds AI data infrastructure for the retail AI boom
Velou, which sells AI-powered self-optimizing product catalogs, raises $5M and adds Gavin Hewitt as COO

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"Product data enrichment" and "product data quality" are used interchangeably in most ecommerce conversations. They are not the same thing. Conflating them leads to misaligned investment decisions — teams that think they have an enrichment program when they have a quality monitoring program, and teams that think quality audits are enrichment when they are not. The distinction is simple but consequential.
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Most ecommerce teams have done some version of product data enrichment. They've written better product descriptions, fixed a batch of Merchant Center errors, or pushed the team to fill in missing attributes before a big launch. But sustained, systematic enrichment — the kind that compounds into a genuine performance advantage — is rare. The reason is not effort. It's a set of recurring mistakes that are deeply embedded in how teams think about and resource the work.
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Your product is only as discoverable as the data describing it. That sentence sounds obvious. But most ecommerce teams treat it as a content problem when it is actually a data architecture problem — and the difference between those two framings determines whether enrichment gets treated as a creative task or a commercial infrastructure investment.

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