Working frameworks, not theory. AI as mechanism, prototyping for the enterprise, design and engineering as one team, the cost curve most pilots ignore. Written as we ship.
Most enterprise AI is still being built as a widget on a page. The harder, more useful question is what mechanism the AI replaces in the operating model. The features die in production; the mechanisms compound.
What we learned shipping the AI Intelligence Layer at Klay Securities. The architecture choices, the compliance constraints, what we'd do differently.
The structural difference between an AI feature that decays and a mechanism that compounds. What it takes to get the feedback loop right at production scale.
Why most enterprise AI doesn't fail at the demo, and what changes when GPU spend, model selection, and eval cost get treated as a system, not an afterthought.
A complete framework for using AI inside the design process, from concept to handoff. What works, what wastes time, how to keep the language consistent.
A working framework for tying design decisions to business metrics. What to track, what to ignore, how to defend design choices in front of the board.
The tools and approaches that hold up at enterprise fidelity. Where rapid prototyping breaks down, and how to push it further without losing rigour.
When the design tool is also the deployment tool. Where Framer holds up against a custom build, where it doesn't, the patterns that keep it shippable.
The case for collapsing the handoff. What changes when the people drawing the screens and the people shipping the code are the same people.
The AI features die in production.
The mechanisms compound.