About
I build AI systems that have to work under real constraints.
My work sits at the intersection of AI infrastructure, platform thinking, and product experimentation. I am most interested in the gap between exciting prototypes and systems that remain useful after the first demo.
That usually means document pipelines, agentic workflows, architecture reviews, integration boundaries, and the practical realities of reliability, cost, and governance.
nkspace.dev exists as a place to think in public about that work: the technical decisions, the tradeoffs, and the lessons that survive contact with production.
Current focus
- Production AI systems and platform architecture
- Agentic workflows with useful operational boundaries
- Document intelligence products and applied experimentation
- Writing about reliability, architecture, and software craft
How I Think
A few principles that show up across the work.
The details change from project to project, but these are the habits of mind that keep returning.
Systems before demos
I care about operational shape: observability, latency, ownership boundaries, failure modes, and what happens when a system is no longer new.
AI as infrastructure
The most interesting work is often in the platform layer: workflow orchestration, review loops, data contracts, cost controls, and reliability.
Writing as engineering
Writing helps sharpen architecture judgment. If I cannot explain the tradeoffs cleanly, I usually do not understand the system deeply enough yet.
Elsewhere
If you’re building in this space too, I’d be glad to connect.
I am especially interested in conversations around applied AI infrastructure, architecture tradeoffs, agentic platform design, and thoughtful product experiments.