Investors are betting on a startup called Qualytics Inc., which believes it can employ artificial intelligence to make sure that other AI models can access the high-quality data they need to improve their accuracy.

The company has just closed on a $10 million Series A funding round, giving it the capital it needs to bring that plan to fruition. The round was led by BMW i Ventures, and joined by new investors Conductive Ventures and The Hill Fund by Firebrand Ventures, plus existing backers like Tech Square Ventures, Knoll Ventures, SaaS Venture Capital, Inner Loop Capital and Rich Family Ventures.

Qualytics is arguing that as enterprises increase their investments in AI-powered automation and data-driven operations, the need to trust their underlying data is becoming more critical than ever. But with so much data flowing into these new systems, it’s becoming very difficult for human data engineering teams to keep pace and ensure the information is accurate and reliable.

To support them, Qualytics is providing AI-powered tools that can proactively manage data quality, including intelligent rule generation, automated anomaly detection and no-code workflows.

The startup says it’s addressing two urgent challenges that have arisen due to the growth of AI – the need to ensure AI models are accessing reliable data, and demands for the democratization of governance across organizations.

Qualytics addresses these issues with its automated platform, which profiles all of the data passing through it to support deep learning at higher scales. The platform works by automatically inferring and evolving data quality rules and by simplifying how any issues that come up are resolved.

It explains that legacy data quality systems have always been reliant on humans, who had to create complicated rules to ensure the health of their data. With Qualytics, these rules-based systems can now be automated to deliver greater data resilience at scale, the company says.

As AI projects take on more urgency, enterprises are realizing that they need to invest more in data quality, the startup says. It cites data from Gartner Inc., which forecasts that 70% of organizations will be looking to automate data quality by 2027, to offset the costs of remediating issues that cost the average company $12.9 million annually.

Qualytics wants to own the data quality automation market, and it believes it’s making good progress in that direction, having recently signed on one of the top-three U.S. financial services institutions as a customer. It has also made progress in terms of integrations, ensuring seamless compatibility with big data platforms such as Databricks, Snowflake, SQL Server, Oracle databases and data catalog tools such as Atlan and Alation.

Qualytics co-founder and Chief Executive Gorkem Sevinc said the company is striving to put automation and usability at the heart of data quality management. “With this new investment and our strong revenue growth, we’re more confident than ever that Qualytics is delivering what modern data practitioners need to manage data quality at scale,” he said.

Constellation Research Inc. analyst Michael Ni said the funding round signals a growing understanding among business leaders that data quality is no longer just a side issue, but foundational for building reliable AI-driven tools.

“The data industry is shifting from reactive to proactive cleansing with embedded observability tools,” he explained. “Qualytics’ AI-driven, automation-first approach is being paired with an ability to unify business and technical users, and that puts it in a strong position as data quality becomes a first-class citizen in modern data stacks.”

The funding from today’s round will enable Qualytics to scale its product and go-to-market teams in order to expand its platform capabilities, onboard new customers faster and bolster sales.

Baris Guzel of BMW i Ventures said Qualytics solves a problem that organizations don’t realize they have, until they suddenly experience a complete breakdown in their data pipelines and the costs hit home.

“The dirty secret in today’s AI arms race is that most models are trained on unreliable inputs,” Guzel said. “It’s just garbage in, garbage out. Qualytics flips that on its head with continuous, automated data quality monitoring at the production layer.”

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