As AI systems take autonomous action, bad data doesn’t just produce bad reports, it drives bad decisions. Qualytics introduces the Data Control Layer to ensure AI systems only act on governed, trustworthy context.

Qualytics, the AI-augmented data quality platform, today launched the Data Control Layer: a new approach to governing the context AI systems reason and act on.

As AI systems move from answering questions to executing decisions, the cost of bad data is no longer limited to inaccurate reports. Bad data now drives automated actions, financial postings, and cross-system workflows at machine speed. Traditional validation models were not designed for this shift: static checks assume predictable data flows, but AI systems retrieve, combine, and act on data dynamically, often without human review.

Qualytics introduces a model it calls validate-at-use, where data is evaluated at the moment it drives decisions. Rather than treating data quality as a set of downstream checks, Qualytics integrates it directly into the context AI systems use to reason and act.

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“The control point for AI has shifted,” said Gorkem Sevinc, Co-founder and CEO of Qualytics. “If validation only happens in data pipelines, you’re already too late. AI systems need to understand whether the context they rely on is trustworthy at the exact moment they reason or act.”

The Qualytics Data Control Layer brings together AI-inferred rules, human-defined policies, anomaly detection, and historical signals into governed, real-time context that can be used by humans, copilots, and autonomous systems alike. Customers today run over 20,000 rules in production on average, with 95% inferred by AI. At its core are three capabilities:

• Augmented data quality coverage where AI handles scale and humans guide governance
• A shared foundation for business teams, data teams, and AI systems to define and apply quality
• Real-time signals that act as controls wherever data is used

The validate-at-use model is designed for how data drives decisions today. Business and data teams work through the Qualytics platform’s purpose-built UX for refining rules, investigating anomalies, and managing governance, with AgentQ adding a conversational interface through natural language. External copilots such as ChatGPT, Claude, and Microsoft Copilot access governed quality signals through Model Context Protocol (MCP), while autonomous systems use the Qualytics API to evaluate data quality and enforce thresholds in real time. Across all interaction models, the same governed context is used.

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“Observability tells you what happened. The Data Control Layer governs what happens next,” said Eric Simmerman, Co-founder and CTO of Qualytics. “We architected quality signals to function as real-time controls that shape how systems behave. That’s not an evolution of observability. It’s a different model entirely.”

As copilots and agents become embedded in core business workflows, the gap between data that’s been validated and data that’s being acted on continues to widen. With the launch of the Data Control Layer, Qualytics establishes a new standard for how data quality operates in the AI era: not as a downstream check, but as a system of controls that governs how AI systems reason and act.