Production Monitoring

Continuous, contextual monitoring designed to surface issues before they impact users or regulators.
We monitor your models for output changes, performance shifts, and suitability breakdowns, without needing access to ground truth.
We help you track model behavior across users, cohorts, and edge cases, revealing where performance diverges most.
Get concise updates that flag risk, summarize trends, and support fast technical or strategic decision-making.

Our Process

Our monitoring solution builds directly into your production pipeline with minimal overhead. We begin by identifying critical outputs and use case-specific thresholds, then tailor drift detection and suitability checks to your real-world context.

Unlike traditional monitoring, we don’t rely on labels or generic metrics. We help you watch for what matters before issues escalate.

Sample Timeline

  • Week 1: Intake session + integration planning
  • Week 2–3: Initial deployment with test run on historical logs
  • Week 4: Live deployment and calibration of alert thresholds
  • Week 5+: Ongoing refinement, reporting, and optional dashboard delivery

Sample Deliverable

  • Live or regularly updated monitoring summary including:
    • Drift detection by output type, cohort, or scenario
    • Alerts for unexpected changes in model behavior
    • Executive briefings and visual summaries for stakeholders
  • Optional: Integration into your existing observability stack (Arize, Weights & Biases, Prometheus, etc.)

Get started with
Production Monitoring
Signal-Based Drift and Degradation Detection
Segment-Aware Monitoring for Real-World Variability
Proactive Alerts and Executive-Ready Summaries
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