Built by a physicist who makes AI systems reason, act, and self-correct.
Owadokun Tosin Tobi
Founder & Lead AI Engineer
Physics undergraduate turned AI Architect. I bridge theoretical physics and production MLOps — building deterministic multi-agent LangGraph systems, physics-informed anomaly detection, and enterprise RAG pipelines that don't hallucinate. My work spans autonomous content agents (NewsAgent Pro), deepfake audio detection (Spectre), quant arbitrage engines (PolyMind), and self-healing MLOps (Sentinel). I don't just use AI — I architect the intelligence behind it.
Physics-Informed Intelligence
Lexpertz exists because standard LLMs hallucinate, drift, and break silently in production. I bridge theoretical physics and production MLOps — building deterministic multi-agent LangGraph systems with adversarial verification, physics-informed anomaly detection, and enterprise RAG pipelines that cite their sources line-by-line. Every system I ship is measured against RAGAS telemetry and validated by a prosecutor node that forces retries until faithfulness exceeds 90%. No black boxes. No magic. Just verifiable, auditable intelligence.
From digital marketing to Physics-informed AI architecture.
The Foundation
Launched Lexpertz Technologies — focused on SEO, digital marketing, and web architecture. The seed of entrepreneurship was planted during what I now call the 'Passive Learning' phase.
The Catalyst: 2 Weeks to 3 Days
Facing a complex Physics concept that traditionally required 14 days to master, I used Generative AI to deconstruct and internalize it in just 72 hours. That moment made the power of AI as a force multiplier undeniable — it became the pivot point of my career.
The Strategic Pivot
Shifted focus from general tech to deep AI Engineering. Began architecting Enterprise RAG pipelines, LangGraph-orchestrated agentic systems, and decision-making AI. Transitioned from using tools to building the intelligence behind them.
Lexpertz AI: The Visionary Phase
The evolution is complete. Architecting SOTA agentic workflows with adversarial verification, physics-informed MLOps, and zero-hallucination RAG pipelines. Shipping open-source projects that prove AI can reason, act, and self-correct — not just generate.