Problem statement
Without monitoring, model behavior drifts quietly and failures are detected only through customer complaints.
continuous assurance
AI model monitoring for performance, drift, and trust continuity.
We monitor model behavior in production with quality, drift, latency, and safety controls tied to real escalation workflows.
What we do
- Monitor model quality, latency, and drift in production environments.
- Track policy violations and unsafe response patterns.
- Implement thresholds and escalation pathways for model incidents.
- Provide governance-ready model performance reporting.
Process
- 1Monitoring baseline and KPI definition
- 2Instrumentation and evaluation setup
- 3Alert and incident workflow rollout
- 4Ongoing drift and quality reviews
- 5Quarterly governance reporting
Tools & frameworks used
MLflowEvidentlyPrometheusGrafanaPythonDataDog
Deliverables
- AI monitoring dashboard
- Drift and quality alert rules
- Incident response runbook
- Governance and trend reports
Need a rapid technical baseline first?
We can run a focused service audit and return a concise execution plan with risk priorities, delivery phases, and control recommendations.