Many platforms are adding Al features: summarizing incidents, generating rules, or answering questions in chat. Helpful-but often bolted onto an operating model that hasn't changed. Alerts still arrive unprioritized. Ownership is still ambiguous. Remediation is still manual. Teams still spend time interpreting what the system already knows.
Prizm is built differently. It is Al native because Al is embedded in the core control plane-how context is gathered, how signals are interpreted, how priorities are set, and how action is orchestrated.
Dedicated AI agents for quality, observability, cataloging, and governance, collaborating to manage diverse data challenges.
Automates data discovery and classification while enriching data with business context for better usability.
Sorts actions into "Autonomous" (fully automated), "Human / AI Assisted" (partial automation), and "Action Needed" (manual).
Identify meaningful connections between data entities across sources, add business context to technical data elements.
Automate determine optimal order based on data lineage and dependencies, data rate changes and business criticality.
Automated profiling frequency with continuous scan data to identify patterns, anomalies, and statistical properties autonomously.
Enables quick issue fixes with one-click remediation, tested safely in staging and overseen by humans when needed.
An animated, interactive graph for exploring schema, volume, and freshness changes while auto-tracing data flows and dependencies.
Maps data issues directly to business KPIs and visualizes their propagation for faster, precise remediation with resolution/fixes.

Consult with a DQLabs specialist about your enterprise Data Observability & Quality needs as well as AI-readiness.
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