Inconsistent schemas, duplicate entities, and missing attributes create a compounding operational burden that limits automation, analytics, and AI-driven workflows.

Claro continuously transforms this fragmented data into canonical, machine-legible entities. The platform ingests heterogeneous inputs, normalizes schemas and attributes, resolves duplicates through entity matching, assigns stable canonical IDs, enriches records with structured context, and validates outputs against taxonomy and policy rules. Clean data is then published back to systems of record and downstream applications via APIs and exports.

By operating above traditional systems of record, Claro functions as a control plane that coordinates data across suppliers, internal teams, and channels. Customers use Claro to accelerate supplier onboarding, improve search and discovery, enable reliable analytics, and support procurement and pricing decisions. Over time, Claro accumulates a canonical entity layer and decision history that compounds in value as more suppliers and categories flow through the platform.

Founded in 2024 by Matteo Fava and Tameesh Biswas (former Delivery Hero and Yelp executives), Claro targets enterprises where product data complexity has become a strategic bottleneckand where making data machine-legible is a prerequisite for scalable AI adoption.