AI that works where
it has to work—
in production.

AI researcher and Azure architect focused on the gap between AI that looks good in demos and AI that makes it into production — higher-than-expected cost, inconsistent outputs, weak retrieval, missing guardrails, and pilots that never reach scale.

Dr. Elnashar is one of the early PhD graduates in the U.S. whose dissertation focused primarily on Generative AI and Agentic AI. His dissertation, Prompt Engineering and LLM Resilience for Software and Data Generation, was completed under Jules White and Douglas C. Schmidt at Vanderbilt — two of the most cited researchers in software engineering globally, with Schmidt holding an h-index of 100+ across 600+ publications. That academic pedigree gives the work both rigor and reach.

Prior to founding Nexoviq.ai, Dr. Elnashar spent years building production ML systems at enterprise scale — including anomaly detection across 12M+ endpoints, reducing inference latency by 70% through architecture optimization. He understands what it costs when AI systems fail in production because he has built systems that could not afford to fail.

He publishes the Enterprise AI Briefing, a weekly LinkedIn newsletter filtering signal from noise for enterprise AI leaders — covering Azure OpenAI, RAG, agents, and AI governance. 163 subscribers and growing, published every Friday.

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