Production-document inspection in an FDA-regulated environment was manual, repetitive, and dependent on careful review. The opportunity for AI was real, but so was the cost of false confidence. The system had to improve consistency without obscuring who owned the final decision.
The approach
I worked with the team to redesign the process and make the source documents easier for both people and machines to inspect. Determinism was treated as a systems problem, not something a better prompt would solve on its own.
What I built
The workflow combined vision-based extraction with rule-driven validation and controlled tool use. Its operating controls included:
- AI-readable document templates
- Tool-gated agents and controlled access patterns
- Prewritten validation scripts and deterministic checks
- Structured extraction from production records
- Explicit exception routing to human reviewers
- Evaluation-driven refinement of prompts, skills, and rules
The outcome
The system automated nearly all routine inspection and shifted the team toward reviewing edge cases and exceptions. Human oversight remained explicit, while each review cycle created evidence for reducing future exception volume.
Why this is signature work
It shows how I approach production AI: start with the process, define the failure boundaries, use deterministic software where possible, and give the model only the work it is suited to do. The model was one component. The surrounding controls made it useful.