Six stages cover one lifecycle. Each is a decision a real AI project meets, in the order it meets them. The studio's full body of work is filed under each one.
Start with Decide →This is everything we know about shipping applied AI. We arranged it the way you will meet it. It covers six decisions, in the order a real project makes them. Insights, case studies and reference pages sit under those decisions, so you do not need to browse by publish date. The decisions are whether to build at all, what shape to build, and how to reach production. Then you decide how to know it works, what breaks once it is live, and what it costs to keep running.
Each stage carries its own argument. It names the decision, the sequence and the mistake most teams make there. It then cites the insights, patterns, failure modes and glossary entries that back it up. Read it front to back if you are scoping a first AI system. Or jump to the stage that matches where your project is.
Every AI initiative starts with a decision most teams never make explicitly: whether the problem is actually an AI problem. Get this call right and the rest of the build gets easier. Get it wrong by default and no amount of good engineering downstream fixes it.
Once "build" wins, the next decision is architectural: a fixed pipeline, an autonomous agent, a retrieval layer, or some combination. Each shape carries a different reliability profile before you write the first prompt.
A working prototype and a production service are different projects with different scopes. The gap between them is usually a bigger lift than the prototype itself, and it's the stage most timelines quietly underestimate.
Shipping without an eval suiteAn eval suite is a versioned set of test cases that measures whether outputs are good. means every prompt or model change afterward is a guess about whether quality went up or down. Evaluate is the stage that turns that guess into a number.
A system that passed every eval still meets a production environment it never saw. That environment holds stale data, ambiguous tool results and untrusted retrieved content. Operate is the stage that catches what ships anyway.
The per-token sticker price on a pricing page is not what an AI feature costs to run. Retries, fallbacks, context growth, and agent loops routinely push real production spend to 5-20x the naive estimate.
Source: https://customlabs.io/handbook/