Validio, an enterprise data management platform focused on AI-ready data reliability, has raised $30 million in Series A funding led by Plural. The round brings total funding to $47 million and will support expansion across the US and Europe while accelerating development of its agentic data management platform.
Addressing the Data Quality Gap in AI
Enterprises are rapidly adopting AI, yet most initiatives fail to reach production due to unreliable data. Traditional data management systems rely on manual rules and static checks that are slow to deploy and difficult to maintain. These legacy approaches struggle to keep pace with AI-driven operations and growing data complexity.
Validio provides an automated platform that helps enterprises monitor and improve data quality at scale. Organizations in data-intensive industries including financial services, manufacturing and telecommunications use the platform to ensure reliable data for analytics and AI.
Patrik Liu Tran, CEO & Founder at Validio, said:
"Most enterprises cannot even trust their data for reporting and analytics, and they are now trying to leverage their data for AI. In the old days, we used to say 'garbage in, garbage out'. In the AI era, everything is magnified: now it’s 'garbage in, disaster out’. It is simply not surprising that more than 95% of AI initiatives never reach production. The fastest way for enterprises to become truly data-driven and implement AI is to fix their data foundation with carefully curated and quality-assured data. We’ve built Validio to be the platform to help enterprises do just that, and we’re excited to partner with Plural as we scale globally.”
Automated Data Management at Enterprise Scale
Founded in 2019, Validio’s platform autonomously detects and resolves data quality issues across billions of records through automated monitoring, anomaly detection and data lineage tracking. The system replaces thousands of manual validation rules while providing full visibility into enterprise data pipelines.
The solution enables organizations to identify issues within minutes rather than during end-of-month reporting cycles, significantly reducing manual investigation and improving confidence in data used for AI and analytics.