AI copilots and autonomous agents now retrieve data dynamically and trigger actions across enterprise systems. But most data quality programs still rely on pipeline checkpoints and downstream monitoring, approaches built for a slower, more predictable world.

In this live demo, Qualytics introduces the Data Quality Context Layer, an extension of the Augmented DQ architecture that embeds governed data quality signals directly into AI reasoning and execution.

Using a validate-before-use model, organizations can evaluate data quality the moment AI systems access it. You’ll see how the Qualytics MCP Server brings quality context into copilots and how the Agentic API enables autonomous agents to enforce data quality thresholds before executing workflows.

What You’ll Learn

• Why traditional data quality models break in AI and agentic environments
• How the validate-before-use model shifts governance to the moment of data use
• What the Data Quality Context Layer provides for AI systems in production
• How MCP and the Agentic API allow copilots and agents to evaluate data quality in real time

Speaker:

Gorkem Sevinc, Co-founder and CEO, Qualytics

Gorkem Sevinc is the Co-Founder & CEO of Qualytics, the augmented enterprise data quality platform. A seasoned technologist and entrepreneur, Gorkem previously co-founded Facet and Scene Health, and was the Co-Founder & Managing Director of the Johns Hopkins Medicine Technology Innovation Center. His work has spanned fintech, digital health, and enterprise data, with a consistent focus on building systems that deliver meaningful impact at scale.

Originally from Cyprus, Gorkem came to the United States on a Fulbright Scholarship. He earned a B.S. in Computer Science from The University of Kansas and an MSE in Computer Science and Surgical Robotics from Johns Hopkins University, where he is now an Adjunct Assistant Professor. He is a frequent speaker on data, AI, innovation, and entrepreneurship. He is an avid Lego builder and lives in Maryland with his wife Dotti, dog Tilly, and cat Luna.

Resource Links:

• Top Data Quality Trends for 2026
• Data Quality vs Data Observability