Practical notes on enterprise AI

Most enterprise AI POCs fail not on the model — they fail at the boundaries. Here's the scoping discipline that separates pilots that survive from pilots that die in the lab.

GPT-class isn't always the answer. We walk through the decision matrix we actually use — closed vs. open-weight, hosted vs. private, and how to think about cost, latency, and risk together.

Inference is rarely the biggest line item. Eval infra, human review, vector storage, and observability are where AI projects quietly burn budget — here's how to plan for it.