About Me
I’ve spent 15 years building production systems that serve millions of users — first in mobile architecture, now in enterprise AI. I hold a PhD in Analytical Chemistry (where I learned that rigorous validation isn’t optional), published iOS Architecture Patterns with Apress, and hold certifications in AI for Enterprises (Wharton) and AI for Product Managers (Duke). My current focus is on the system-level engineering problems that make or break AI in production: retrieval quality, observability, guardrails, and regulatory compliance.
What I Focus On
My current work centers on the system-level challenges of enterprise AI:
- Designing RAG (Retrieval-Augmented Generation) architectures
- Defining evaluation strategies for LLM-based systems
- Improving observability and debugging capabilities
- Analyzing cost, latency, and scalability trade-offs
- Identifying failure modes in production environments
How I Think About AI
AI systems are not just models.
They are pipelines composed of data, retrieval mechanisms, prompts, models, evaluation layers, and feedback loops.
Most failures don’t come from the model itself, but from how these components interact.
Understanding those interactions is where real engineering begins.
This Site
This is not a tutorial blog.
It’s a collection of architectural patterns, failure analyses, and system-level insights — all focused on one core question:
Why do AI systems fail in production — and how do we design them to be reliable?
Follow the Work
I share shorter observations on RAG architecture, EU AI Act developments, and enterprise AI failures on LinkedIn and X. No hype. No tutorials. Just what I’m seeing from the inside.
