Conduktor Trust Validates Streaming Data Before It Breaks Your AI

Operational data powers GenAI, LLMs, agentic AI, and real-time analytics. But most teams lack the tools to govern this data at ingestion. By the time bad data reaches downstream systems, it has already derailed decisions, corrupted AI outputs, and broken customer experiences.

Proactive Data Quality for Kafka

Conduktor Trust validates operational data in motion. Unlike legacy governance tools that react after the damage, Trust validates data for completeness, context, and structure before it enters your systems.

"AI decisions can't be trusted if the data isn't," Conduktor CEO Nicolas Orban said. "By enforcing data quality as it flows, Conduktor becomes the trust layer AI needs."

Organizations using Trust get reliable AI outputs, better customer experiences, and lower costs. Removing low-quality data after ingestion is expensive. Blocking it at the source is cheap.

How Trust Works

Data stewards and governance teams operationalize governance at scale:

• Define quality rules: Use CEL (Common Expression Language) to write precise rules enforcing structure, completeness, and conformance.
• Apply policies centrally: Administer rules across your entire Kafka environment instead of devolving responsibilities to individual teams.
• Trigger real-time actions: Log or block data instantly when it breaks rules.
• Gain operational insights: Track violations over time to identify patterns and prioritize fixes.

Use Cases

Trust guarantees operational data is trustworthy, accurate, and consistent for:

• AI and ML pipelines: Validate streaming inputs to prevent drift, bias, and hallucinations in GenAI, RAG, and LLMs.
• Real-time personalization: Verify customer behavior data for dynamic pricing, recommendations, and individualized experiences.
• Predictive maintenance: Enforce consistency in telemetry data to optimize forecasting and reduce downtime.

Get Started

Trust is available in private preview. Request access at conduktor.io/trust.