Resources
Field notes from the arena
Practical writing on getting AI into production — delivery models, security, and the realities between a prototype and a system you own.
What a forward deployed engineer does before the first line of code
The most consequential work in an AI engagement happens before anyone opens an editor. Here is what an embedded engineer actually does first — and why skipping it is how projects fail.
May 17, 2026 · Zachary Meyer
Own what we build: designing for handoff from day one
No lock-in is not a courtesy we extend at the end of an engagement. It is a constraint that shapes the architecture from the first decision. Here is why, and what it looks like in practice.
May 16, 2026 · Kenneth Starling
Security can't be the last sprint
Treating security and compliance as a gate at the end of an AI project is how prototypes die in review. Here is how we make it a phase, not a postscript.
May 14, 2026 · ArcusForward
Why most enterprise AI never reaches production
The bottleneck in enterprise AI is almost never the model. It is the distance between a prototype and a secure, integrated, owned system — and the operating model that closes it.
May 11, 2026 · ArcusForward
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