GRANICUS • AI IMPLEMENTATION QUALITY
AI Implementation Quality
Define trustworthy behavior, test it against evidence, and turn failures into durable controls.
High-consequence QA and validation experience combined with hands-on AI workflow evaluation, source controls, and human review.
85 / 57
DOE-linked validation tasks / deficiencies
3 / 17
management-control tools / functional checklists
Wrong source → control
regression converted into standing verification gates
CASE 01
Wrong-source regression → permanent controls
A source-identification failure reintroduced an obsolete historical source into an AI-assisted workflow.
I restored the correct base and added source-identity, completion-state, conflict/uncertainty, correction-record, and stop-and-verify controls.
CASE 02
Formal validation with the Department of Energy
Worked directly with the U.S. Department of Energy to review two nuclear technical orders; executed 85 proficiency tasks, identified 57 deficiencies, and authenticated Broken Arrow procedures.
The Air Force Commendation Medal citation credits the work with the first Broken Arrow response-procedure update in 20 years.
CASE 03
Model-role boundaries and evidence arbitration
Separated AI roles for research, drafting, independent review, completion-state checking, and human decision authority. Conflicts return to canonical evidence rather than model majority.
CASE 04
Formal QA ownership
Led Wing Inspection Team and formal QA/self-assessment across three management-control tools and 17 functional-area checklists.
Supporting evidence
- 88-page / six-instruction requirements audit.
- Separate >45%, 40%, and 33% readiness/training improvements.
- Separate 37% internal-control discrepancy reduction tied to $313K equipment/readiness work.
- ATAK implementation across three wing agencies; separate 50% ACE + EMUD result.
- 64 reports → 57 realistic training devices → 24 operations / 34 personnel.
Scope & boundaries
I have not worked inside a production LLM annotation platform or owned a production AI quality dashboard/inter-rater calibration program. This page focuses on source-grounded QA, evaluation, failure recovery, representative scenarios, and human review I can substantiate directly.
Operating pattern
Understand → Define the standard → Test → Diagnose → Control → Implement → Re-test → Measure → Improve.
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