Qualytics, an AI-augmented data quality platform, has officially launched the Data Control Layer. This new framework introduces a "validate-at-use" model, moving data quality from a downstream pipeline check to a real-time system of controls. As AI systems evolve from simple assistants to autonomous agents capable of executing financial postings and system workflows, Qualytics aims to ensure that the context driving these machine-speed decisions is trustworthy at the exact moment of execution.
Validate-at-Use Model: Data is evaluated at the moment it drives a decision, rather than just during ingestion or storage.
AI-Inferred Rules: 95% of the average 20,000 rules run by customers are automatically inferred by AI, reducing manual governance overhead.
MCP Support: External AI such as ChatGPT, Claude, and Microsoft Copilot can access quality signals via the Model Context Protocol (MCP).
AgentQ Interface: A natural language conversational interface that allows business teams to refine rules and investigate anomalies.
Real-Time API: Autonomous systems can use the Qualytics API to enforce quality thresholds and stop actions driven by "bad data" in real time.
Shift from Observability: Unlike traditional observability (which tracks what happened), the Data Control Layer governs what happens next.
Traditional data validation assumes predictable, static data flows. However, modern AI systems retrieve and combine data dynamically, often bypassing human review. Qualytics addresses this by integrating quality signals—including human-defined policies and AI-driven anomaly detection—into a unified context. This ensures that whether a human, a copilot, or an autonomous agent is acting on data, they are all operating from the same governed, verified foundation.
The Data Control Layer is purpose-built for the "agentic era." By using the Model Context Protocol (MCP), Qualytics provides a standardized way for LLMs to securely "check" data quality before summarizing a report or executing a command. This reduces the risk of AI hallucinations or errors driven by stale, incomplete, or inaccurate data, which can have significant financial and operational consequences when automated at scale.
"The control point for AI has shifted. 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." — Gorkem Sevinc, Co-founder and CEO of Qualytics
"Observability tells you what happened. The Data Control Layer governs what happens next. We architected quality signals to function as real-time controls that shape how systems behave." — Eric Simmerman, Co-founder and CTO of Qualytics
About Qualytics
Qualytics is the data control layer for trusted context. The platform combines AI-augmented data quality with human governance to validate data when it's used, delivering governed signals as controls across analytics, applications, copilots, and agents.
