Observability
The capability to infer an AI system’s internal state and behavior through collection and analysis of logs, metrics, and outputs for effective monitoring and troubleshooting.
Goes beyond basic monitoring to provide deep insights into system health. Observability pipelines collect structured logs (requests, errors), metrics (latency, resource use), and traces (execution paths) from data-ingestion, training, and inference services. With correlations and dashboards, teams can pinpoint root causes of issues, replay events, and perform post-mortems. Governance defines which signals to capture, retention policies, and alert thresholds to maintain system reliability and compliance.
A fraud-detection platform integrates OpenTelemetry to emit traces for every transaction, logs model decisions with confidence scores, and tracks CPU/GPU usage. When latency spikes, the SRE team drills into traces to discover a slow feature-store query, fixes the indexing, and restores normal performance within 15 minutes.
“What used to take weeks of manual reviews and policy work is now structured and auditable in Enzai within minutes. It’s the first time AI governance has felt operational, not theoretical.”
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What problem does Enzai solve?
Enzai provides enterprise-grade infrastructure to manage AI risk and compliance. It creates a centralized system of record where AI systems, models, datasets, and governance decisions are documented, assessed, and auditable.
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Empower your organization to adopt, govern, and monitor AI with enterprise-grade confidence. Built for regulated organizations operating at scale.