Co-Founder & CTO (NitroFix) — AI Output Validation and Scientific Evaluation in R&D
Served as CTO while integrating AI tools into R&D workflows and performing systematic validation of AI outputs against scientific principles, experimental data, and engineering constraints. Focused on identifying hallucinations, inconsistencies, failure modes, and feasibility risks to produce reliable, evidence-based insights. Developed structured evaluation approaches for technical feasibility and experimental validity under high uncertainty. • Validated AI responses by comparing them to experimental and engineering evidence • Performed artifact detection and inconsistency/hallucination identification • Iterated prompts/workflows to improve scientific soundness • Conducted technical due diligence-style reviews of model and research claims