Agentic enterprise data management platform that automates data observability, quality, lineage, and cataloging to ensure trusted data for analytics, AI, and reporting.
Investment Rounds
Capital activity and funding progression
| Amount | Date | Round |
|---|---|---|
| $30 M | Mar, 2026 | series-a |
Industries
Frequently Asked Questions
Where is Validio located?
Validio is located in Stockholm, Sweden.
What industries does Validio operate in?
Validio operates in the following industries: data management, enterprise software, data quality, data observability, artificial intelligence.
When was Validio founded?
Validio was founded on 2026-03-05.
How much total funding has Validio raised?
Validio has raised a total of $30,000,000.
Who are the investors in Validio?
The investors in Validio are: Plural, Lakestar, J12, Kevin Ryan, Denise Persson, Emil Eifrem, Sven Hagströmer.
Who are the founders of Validio?
The founders of Validio are: Patrik Liu Tran.
What is Validio and what does it do?
Validio is a comprehensive data observability platform designed to help organizations validate, monitor, and improve the quality of their data in real-time across the entire data pipeline.
How does Validio detect data quality issues?
Validio uses automated machine learning models and threshold-based checks to identify anomalies, schema changes, and distribution shifts, ensuring that data teams are notified of issues as they occur.
Which data sources can be integrated with Validio?
Validio supports a variety of modern data stack integrations, including cloud warehouses like Snowflake and BigQuery, streaming services like Kafka, and various relational databases.
How does Validio handle real-time data monitoring?
Validio is built for speed and can monitor data at the source or in-flight, allowing for immediate detection of quality incidents before they impact downstream applications or business decisions.
What are the primary benefits of using Validio for data teams?
Validio enables teams to increase trust in their data, reduce the time spent on manual debugging, and prevent broken dashboards or failed machine learning models by automating the observability process.