Before Claro, our team spent years building and operating data platforms inside large, fast-growing companies.

We saw the same issue everywhere : product and supplier data was fragmented across feeds, files, documents, and systems — and keeping it consistent required constant manual effort. Teams built scripts, rules, and one-off fixes, but the work never ended because the data never stopped changing.

At the same time, AI made it easier to extract and enrich information — but it didn’t solve the operational problem of keeping data reliable across systems. Claro was born from that gap. Instead of creating another system of record, we built an execution layer that sits above existing tools — continuously cleaning, standardizing, validating, and syncing operational data back into the systems teams already use.