Case Study
ML Observability Rollout for Workflow Automation | Shawn Lawyer
Productionized ML-backed workflow automation with model health tracking, alerting, and cost controls.
Constraints
- Multiple model providers with different reliability characteristics
- Limited telemetry for model quality over time
- Strict budget limits on inference spend
Outcomes
- Delivered a measurable quality baseline for model outputs
- Established incident response playbooks for AI-specific failures
- Enabled safer weekly iteration of prompts and model versions
Key Metrics
- 29% improvement in task completion quality
- 34% reduction in inference spend per completed workflow
- 52% faster incident triage for model regressions
Implementation Notes
The client had successful experiments but no operational structure. The rollout created reliable monitoring and decision thresholds before scaling volume.
Operating principles
- Measure output quality with business-linked indicators.
- Alert on drift before users report failures.
- Keep model costs tied to value outcomes.