Operational data—the real-time information that flows through applications, services, and user interactions—fuels modern applications, such as GenAI, LLMs, agentic AI, and real-time analytics. Despite its importance, many teams lack the right tools to govern this data at the point of ingestion, before it contaminates downstream systems, derailing real-time decisions, corrupting AI outputs, and breaking customer experiences.

Today, Conduktor launched Trust, the first product that guarantees the quality of operational data in motion. In contrast to legacy governance tools that take a reactive approach, Trust works proactively, validating data for completeness, context, and structure.

“AI—Generative and Agentic—are not just buzzwords for us,” Conduktor CEO Nicolas Orban explained. “They’re part of a much bigger mission: unlocking the value in our customers’ data. But AI decisions can’t be trusted if the data isn’t—especially when AI products lack access to real-time, trustworthy data and operational governance. By enforcing data quality as it flows, Conduktor becomes the trust layer AI needs.”

Using Conduktor Trust, organizations can guarantee reliable AI outputs, improve customer experiences, optimize algorithmic performance, and significantly reduce costs—because it is much more difficult and expensive to remove low-quality data after it’s made its way into your environment. For organizations, this strengthens brand reputation, simplifies SLA adherence, and speeds up decision making.

With Conduktor Trust, data stewards and governance teams can operationalize governance at scale by:

• Defining quality rules: Use CEL (Common Expression Language) to write clear, precise rules that enforce structure, completeness, and conformance.

• Applying policies centrally: Administer rules across your entire Kafka environment, rather than devolving responsibilities to teams or individuals. This helps encourage best practices around visibility and data quality while reducing risk and increasing compliance.

• Triggering real-time actions: Choose what happens when data breaks the rules—log it or block it instantly.

• Gaining operational insights: Track violations over time to identify patterns, prioritize fixes, and raise the bar on data quality.

These quality rules are then applied as policies to different resources. When violations occur, Trust can take action, either blocking the message from being consumed, or reporting it to a human owner for further investigation.

In addition, Trust also provides centralized monitoring and control. Users can configure rules, policies, and actions across their Kafka environment; visualize evaluations and violations; and foster a strong culture of data quality. 

Conduktor Trust guarantees that operational data is trustworthy, accurate, and consistent for mission-critical systems, including: 

• AI and machine learning pipelines: Validate streaming inputs to prevent drift, bias, and hallucinations in GenAI, RAG, agentic AI, and LLMs.

• Real-time personalization at scale: Verify high-quality, real-time customer behavior data for dynamic pricing, recommendations, and individualized experiences.

• Predictive maintenance algorithms: Enforce consistency in telemetry data to optimize forecasting, improve repair schedules, and reduce downtime.

By ensuring data quality and consistency, Trust transforms operational data into a strategic asset for AI and automation. Conduktor Trust is available in private preview today—to request access or learn more, visit conduktor.io/trust.