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.)
