Patrik Liu Tran / Validio
Poor data has long been an expensive, if unglamorous, problem inside large organisations. As companies generate and store ever larger volumes of information, ensuring that data is accurate, complete and usable has become increasingly difficult.
Now, with the rapid adoption of AI across enterprise software, the issue is becoming even more acute. AI systems depend heavily on large datasets to train models, automate decisions and power new services — but if that underlying data is flawed, the results can be unreliable or even harmful.
Research from Gartner suggests that data quality and availability remain among the biggest obstacles to implementing AI at scale. Enterprises may invest heavily in data infrastructure and machine learning systems, but those investments often stall before reaching production because the data feeding them is incomplete, inconsistent or corrupted as it moves through pipelines.
For Swedish startup Validio, this growing problem represents a major opportunity. The company has raised a $30m Series A round led by Plural, as demand grows for tools that help companies ensure their data is trustworthy.
Previous investors and angels including Lakestar, J12, MongoDB’s co-founder Kevin Ryan, Snowflake’s CMO Denise Persson and Neo4j’s co-founder Emil Eifrem also participated in the round, bringing the startup’s total funding to $47m.
The data velocity is so huge. There is no way that we as humans can manually go through all of this
CEO Patrik Liu Tran founded Validio in 2019 with the aim of solving one of the most persistent challenges in enterprise data: ensuring that information remains reliable as it moves through increasingly complex data systems.
“Data management is one of those tasks where you’re not really replacing people, you’re doing work that never was done to begin with,” Tran says in an interview with Pathfounders. “95% of enterprise data is of poor quality. That’s just how it is out there. The absolute overwhelming majority of data out there is of poor quality, and why is no one taking care of that?”
Before founding the company, Tran spent years advising large organisations on their data and AI strategies. Tran says he built around 20 internal data consistency tools for different companies before deciding the problem required a dedicated platform. That realisation ultimately led him to build Validio — the name shot for valid input and output.
Today the startup describes itself as an agentic enterprise data management platform designed to automate the monitoring and validation of enterprise data. The platform includes more than 90 automated validators that check datasets for common quality issues, such as whether data is accurate, whether it has been distorted while moving between systems, whether it is up to date, whether pipelines have been disrupted and whether datasets are complete.
Part of the challenge is scale. As businesses adopt cloud infrastructure, streaming pipelines and real-time analytics, the speed and volume of data production have grown dramatically — making manual monitoring almost impossible.
“The data velocity is so huge and the amount of data that’s being produced is growing exponentially over time,” he continues. “So there is no way that we as humans can manually go through all of this when we couldn’t even do it a decade ago. So I think the agentic capabilities makes things much more scalable, even more than before.”
Validio’s platform can be deployed in several ways depending on customer requirements, including cloud environments, fully isolated virtual private clouds or on-premise installations. This flexibility is particularly important for highly regulated industries where sensitive data cannot leave secure infrastructure.
The company’s customer base already includes organisations such as Canva, Nordea, Deutsche Glasfaser, Truecaller, Surfshark, Walden and AllianceBernstein. Many of their customers operate in sectors where reliable data is critical to operations, including financial services, telecommunications and manufacturing.
These industries are also increasingly experimenting with AI-driven automation, making the reliability of underlying data even more important.
“Data quality is no longer a nice-to-have — it’s mission-critical for AI success,” Persson said in a statement. “Validio tackles the data trust challenge head-on with an AI-powered quality and lineage platform for all types of data, AI and agentic workloads.”
While Validio’s main competitors largely remain traditional data management tools, there are a handful of newer startups building dedicated platforms focused specifically on data reliability.
Superconductive, which created the open source data quality tool Great Expectations, raised $40m Series B funding in 2022, bringing its total raised to $61m. In the same year, another data observability startup, Monte Carlo, raised $135m Series D at a $1.6bn valuation.
With its new funding, Validio plans to expand its presence internationally, particularly in the US and UK. The company is setting up offices in New York and London while continuing to grow its engineering team in Sweden.
Its focus remains primarily on large enterprises, particularly those in the Fortune 2000.