Veridion Raises $20M Series A for a Live Global Business Data Graph
The AMW Read
The Series A incrementally strengthens a company-data infrastructure entrant whose differentiation rests on fresher, continuously updated entity and relationship data.
Veridion Raises $20M Series A for a Live Global Business Data Graph
Veridion has raised a $20 million Series A led by Hoxton Ventures, with participation from existing investors Underline Ventures, OTB Ventures, Gapminder, Day One Capital, and LAUNCHub. The company said it will use the funding to accelerate AI-driven digital replicas of businesses and expand what it describes as a real-time map of corporate activity. Veridion says its live business graph covers 640 million companies globally, while its API documentation describes weekly updates for more than 180 million companies. Valuation and total funding were not disclosed.
The pitch targets a durable weakness in enterprise data: company records used for credit decisions, insurance underwriting, supplier selection, third-party risk review, and ESG assessment can lag operational reality by quarters or longer. Veridion gathers signals from company websites, public corporate registries, regulatory filings, online product catalogs, social profiles, and trusted news sources, then uses them to continuously update company profiles. Its thesis is that business data should operate as a changing graph of entities, products, activities, and commercial relationships rather than as a periodically refreshed directory.
For builders, the important test is not merely data coverage but whether updates are accurate enough to become inputs to operational workflows. A weekly or continuously refreshed corporate graph could help procurement and risk teams identify exposed suppliers faster during disruption, but it must also provide auditable provenance and reliable entity resolution across jurisdictions. For investors, the Series A is a focused bet on data infrastructure that can sit beneath market intelligence and enterprise decision systems, where the defensibility will depend on freshness, coverage, and the quality of the underlying relationship graph.