I know the technology.
I build the trust.
I am the technical closer.

Most can do the parallelism math. Very few can connect it back to choices of RDHX, PG-25 liquid cool, or two-phase DTC cooling. The conversation a customer building an AI factory actually needs runs the full chain — from model arithmetic through cluster topology to power and cooling. I want to be able to hold both ends of that conversation.

Fine-tune for form, RAG for facts. LoRA shifts behavior — style, format, domain fluency, task specialization. It's a weak tool for installing facts that contradict strong pretraining priors. NVIDIA's NeMo Framework + NeMo Retriever stack is designed for exactly this combination — and it's the conversation NCP customers asking "should we fine-tune Nemotron?" actually need to have.

AI infrastructure conversations are shifting from "how big a model can we run?" to "how does this agentic workload behave under load?" The two are very different problems. Building agents from scratch is the only way to answer the second one honestly.

It's been my best classroom for understanding what AI infrastructure actually has to support. Latency budgets matter when prices move in milliseconds. Memory access patterns matter when you hold state across thousands of markets. Observability matters when something's bleeding and you don't know which strategy. Most of what I sell becomes concrete when you have to operate it yourself.

Attention is a memory-bandwidth problem before it's a compute problem. Context length is a quadratic cost. Pretraining is mostly babysitting. These are the things that turn into "what's your inference budget?" questions in a sales call — and I can now answer them from the inside.