Engineering perspectives on data & AI.

Most AI failures are not model failures. What an AI Architecture Review reveals about routing, boundaries, governance, cost, and what to leave alone before the next model upgrade.

AI governance changes should ship like infrastructure, not like ad hoc configuration edits. How to use shadow, canary, and production rings with promotion gates and rollback.

Most enterprise AI failures are not about model quality. They are about routing, classification, safety, and audit. Why the control plane matters more than the next model upgrade.

When internal and external AI workloads share the same path, data leakage becomes an architecture problem. How to design route segregation that makes leakage physically impossible.

Shopify built the agent commerce layer. Most stores are still optimized for traditional search. Here's what the AEO gap looks like and what AI-ready commerce requires.

Why we treated content as an operating system instead of an editorial calendar, and the architecture we built to turn internal work into authority and inbound.

WebMCP is a new browser-native API that lets websites expose structured tools to AI agents. Here's what it means for your business, and what to do about it.

The failure patterns are almost never about the models. After building production AI systems, we've identified what actually goes wrong, and how to fix it.

Self-hosted open-source AI infrastructure is technically mature and dramatically cheaper than cloud APIs. Here's when it makes sense, and when it doesn't.

The highest-value enterprise AI operates invisibly, processing, deciding, and routing without a prompt. Why the chatbot mental model leads to underinvestment in the systems that actually matter.