From idea to shipped AI — the exact method we use.
Most AI projects don't fail because the model isn't good enough. They fail because they were never scoped to ship. This is the method we use to take a build from "we should use AI for that" to a system running in production. Six stages.
The common mistake is starting from "let's build an AI agent" instead of "what specifically gets better." Flip it. State the outcome as a measurable change — hours saved, error rate down, response time cut, throughput up. If you can't put a number on it, you're not ready to build.
Scope, Build, Guard, Deploy, Compound — the five-question scoping checklist and the four guardrails. One email, the whole method.
The common mistake is building a platform when you needed one workflow. Cut ruthlessly. Before you write a single line, run the five-question scoping checklist:
If you can't answer all five, you're not scoped yet. Most builds that die, die right here.
Build against real data and real tools from day one. Ship ugly but working — a rough system doing the real job beats a polished demo doing a fake one. The point of version one is to prove the workflow survives contact with reality, not to look finished. The common mistake is polishing the interface before the core workflow actually works.
This is the stage most teams skip, and it's the one that decides whether you can actually deploy. An AI system will be wrong sometimes. What matters is whether you catch it before it causes damage. Before anything touches real users or real money, put four things in place:
Test cases with known right answers, re-run every time you change the system.
Hard limits on what the system can do and what it can touch.
An automatic way to know when it's wrong — not a hope that someone notices.
A person reviews before an action is final, until the system earns trust.
Put it in front of a narrow slice of real work first. Instrument everything — what it did, where it failed, what a human had to correct. Watch it closely for the first weeks. Width comes after you've watched it survive narrow. The common mistake is launching wide before you've earned the right to.
Once it works, extract what's reusable — the scoping, the guardrails, the prompts, the architecture — so your next build starts at stage three instead of stage one. This is how one working agent quietly becomes an AI-capable organisation. The first build is expensive. The tenth should be cheap.
Most AI builds die at scoping, not at the model. Frame the outcome, cut to one workflow, and invest in guardrails before polish. Do that, and you ship.
Run it on a real problem with us →