Client work
ML Observability Rollout for Workflow Automation | Shawn Lawyer
Implemented observability, operational controls, and inference cost visibility for AI-powered workflow automation.
What mattered most
- Multiple model providers behaved differently in production
- The team had limited visibility into quality over time
- Inference costs had to stay within a strict budget
Results
- The team gained a clear quality baseline for model output
- AI-specific response and support playbooks were put in place
- Weekly prompt and model updates became safer to manage
Key numbers
- 29% improvement in task completion quality
- 34% reduction in inference spend per completed workflow
- 52% faster incident triage for model regressions
How the work moved forward
The team had promising AI experiments and needed the controls to support them in day-to-day operations. The work focused on visibility, safer iteration, and better cost control.
What changed
- Added business-linked quality measurements.
- Added alerts and dashboards for drift and reliability changes.
- Connected model costs more closely to value and usage outcomes.
Result
The team gained a stronger operating model for production AI systems and a more durable foundation for safe growth.