Articles
Notes from production.
Teardowns, operational notes, and the patterns I've seen survive a quarter in real workloads. No cadence promise - published when there's something worth writing down.
What a forward deployed engineer actually does - and whether you need one
The hottest title in AI is mostly a description of work you can already buy. The three stages of the job, why the skillset is genuinely rare, and what to do if you want the outcome without the hire.
The documented process is never the real process
Every automation project starts with someone describing a workflow. The description is usually the clean version - and a system built from the clean version automates a job nobody actually does.
Where AI does not belong in your business
The most useful question in an AI project is not "what can we automate" - it is "what should we leave alone." A step-level triage for deciding where a model earns its place.
The demo is the easy part: exceptions, evals and evidence
There is one way a workflow goes right and a thousand ways it goes wrong. What separates an impressive AI demo from a system you can actually trust with your operation.
Build on the stack you have - why "first, migrate" is a red flag
When an AI proposal starts with replacing your ERP or CRM, someone is optimizing for themselves. The case for building intelligence on top of the systems you already run.
Shadow mode first: how an AI system earns autonomy
Nobody sane flips a switch and lets an agent run their operation on day one. The staged rollout that turns "trust me" into "look at the numbers."
Don't marry a model: why your AI setup should survive a swap
Clients ask "which model do you use?" It is the least important question. What matters is how cheaply you can change the answer when the market moves - and it moves every few months.
How to automate your business with AI - the actual process
Not the tools - the order of operations. The sequence that takes a workflow from "described in a meeting" to running in production, and why each step exists.
Why most AI automation projects break in production
AI workflows pass demo, ship to production, and quietly break three weeks later. Five failure modes I keep seeing, and the patterns that survive.
Human-in-the-loop workflows: the part everyone skips
The pitch is that AI removes the human from the loop. The version that holds up in production almost always puts a human back into it - at exactly one step.
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