Six stages, one lifecycle: the decisions a real AI project actually meets, in the order it meets them, with the studio's full body of work filed under each one.
Start with Decide →This is everything we know about shipping applied AI, arranged the way you'll actually meet it: as six decisions, in the order a real project makes them, rather than a list of insights, case studies, and reference pages sorted by publish date. Those decisions are whether to build at all, what shape to build, how to get it to production, how to know it works, what breaks once it's live, and what it actually costs to keep running.
Each stage carries its own argument: the decision, the sequence, the mistake most teams make there. It then cites the specific insights, patterns, failure modes, and glossary entries that back it up. Read it front to back if you're scoping a first AI system, or jump straight to whichever stage matches where your project actually 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 — and 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 suite 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 full of stale data, ambiguous tool results, and untrusted retrieved content it was never tested against — 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.