Free resource

AI Project Readiness Checklist

A 20-point checklist to stop your AI project from stalling between demo and production. Get the PDF + weekly field notes.

What you'll get

✓20 actionable checks covering strategy, engineering, security, and operations.
✓Deployment readiness score — know exactly where your project stands.
✓Common failure patterns and how to avoid them.
✓Weekly Notes from the Edge — field notes on AI engineering, security, and performance.

Inside the checklist

1. Business outcome is defined and measurable.
2. Success metric exists before model selection.
3. Cheapest viable model is chosen first.
4. Tiered agent architecture is documented.
5. Eval pipeline runs before every deploy.
6. Regression tests exist for model outputs.
7. CI/CD is reproducible, not local-only.
8. Prompts and model configs are versioned.
9. Security review happens before launch.
10. Least-privilege access is enforced.
11. Input/output filtering is in place.
12. Audit logging covers model decisions.
13. Kill switch exists for agentic workflows.
14. Data residency and compliance are mapped.
15. Cost alerts and budget caps are configured.
16. Semantic caching reduces redundant API calls.
17. Batch processing handles non-urgent work.
18. Rollback plan is tested and documented.
19. Owner assigned for monitoring and maintenance.
20. Drift detection and retraining plan exist.

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