Six rules that decide whether an AI-built product survives production. Full text, with checklists you can run today, whether you built the thing yourself or it landed on you when the builder moved on. You can read the guide without giving us your email. The hard part is applying the rules.
AI Delivery is the work of taking AI-generated products to production reality.
AI Delivery checks the generated work and finishes what production requires. It also assigns an owner. At the end there’s a product that works and someone who owns it.
AI made building cheaper. Verifying the result still takes work. The gap between what runs in a demo and what holds on a Monday is where most AI products stall. The six rules cover the work needed to close that gap. Each rule comes from a production problem we have seen.
These are the same rules published on the homepage, word for word. Read each one, then run its checklist against your own product, honestly. The "you" here is whoever holds the risk now: the founder who prompted the thing into being, or the person it landed on when the builder moved on. Start with any box you cannot check.
You asked AI if your product was ready. It said yes. You asked again, differently, forty times. Yes, every time. And somewhere in there, a quiet voice said: in theory.
Take that voice seriously. Not because doubt is always right, but because it points at something untested. Doubt isn’t a verdict. It’s a question that hasn’t been run yet.
So we run it. Every scope starts by turning your doubts into tests: name the fear, define what would prove it wrong, go find out. Doubt in, evidence out. Bring it here. It’s on the sign.
The demo is real. It got people leaning in, and that matters. But shipping means surviving what demos never face: a security questionnaire, a traffic spike, a Monday.
Vibe-built apps land about 70% complete, polished enough to look nearly done. The missing 30% is the unglamorous part: auth, data integrity, scale, cost per request, fallbacks, what happens when the model is wrong. Columbia researchers vibe-coded more than fifteen applications and sorted hundreds of failures into nine patterns. The two worst were error handling and business logic.
That’s the part we build. It’s easy to defer from the inside, and it decides everything.
Somewhere in your company there’s a product AI built that nobody can fully explain. It runs. It matters. And if it breaks at 2am, no one’s name is on it.
Ownership isn’t a feeling. It’s a spec: a named accountable person, documentation your team actually runs on, monitoring, an incident plan, and a decision about who answers when the model or the vendor changes underneath you.
Twenty-five years of scoping comes down to one rule: if you can’t support it, you’ve got no business shipping it. Everything we deliver ships with an owner. In writing. No orphans.
AI can make a confident case for almost any answer. Confidence is not evidence. A bluff is exactly that: confidence without evidence. So we don’t argue. We test.
The rubric is written and disclosed: architecture under load, security under attack, the workflow with real users in it, costs at real volume. The same dimensions every time, with tests and thresholds matched to the product’s risks, so the verdict comes from evidence, not from a veteran’s hunch.
And because we also build, we say it plainly: the scope is priced to stand alone, no-go is always an acceptable answer, and the verdict is yours to take anywhere. Judgment you can’t afford to hear isn’t judgment.
Working isn’t the same as worth it. Plenty of AI systems run perfectly and produce nothing: the demo impressed, the workflow never changed, the money never noticed.
So we measure the boring way: against the non-AI alternative, in real workflows, with real users, counting the errors and what they cost. Before anything gets built, we agree in numbers what paying off means.
If it doesn’t pay, we say so. That’s the cheapest sentence we’ll ever sell you.
You know the loop: one more prompt, one more version, almost right, again. The loop feels like progress. It’s motion.
Done is not a feeling. Ask any artist: putting the brush down never feels right. Done is a line drawn in advance: acceptance criteria agreed before the iteration starts, and kill criteria for when to stop entirely.
We draw that line before we begin, and we hold the work to it. Ours included. Done is a gate, not a wall: the product ships, gets owned, and earns its next round. Closure ends the lap, not the road. That’s the job.
Each one is a question that hasn’t been run yet. If you want a second set of eyes on yours, that’s the free assessment: ten minutes of your time, a straight answer within two days, no charge, no obligation. You can also send a system whose original builder is gone. Whoever built it, you hold the risk now. The answer is yours either way, including a no.
Let’s find outPrefer to keep going alone? Good. That’s what the checklists are for.