METHODOLOGY

Production-Ready

Software you can put in front of real users without getting paged at 3am: authorization on every endpoint, validated input, error handling, rollbacks.

Last reviewed: byChristof Jori wiki ↗

Production-ready is the difference between software that works when you demo it and software that works when you are asleep and a stranger is using it wrong. It is not a feeling and it is not a milestone you declare. It is a concrete checklist, and the gap between a demo and production-ready is exactly the list of things that have no visible payoff right up until the moment they fail.

The checklist, stated plainly: authorization on every endpoint, not just authentication on the login; input validation on everything that crosses a trust boundary; no secrets in the client bundle or the repo; error handling that fails safe instead of leaking a stack trace; a regression suite that runs on every change so the next fix does not reopen an old bug; observability so you find out from a dashboard, not a furious customer; and a rollback path measured in minutes. Miss any one of these and you have a demo wearing a production costume.

Worked example. Two versions of the same booking app look identical in a demo. Version A returns a booking by ID after checking only that you are logged in. Version B checks that the logged-in user actually owns that booking, validates the ID format, logs the access, and alerts if one account starts enumerating IDs. Same screens, same happy path, same demo. Version A is a breach waiting for someone curious; Version B is production-ready. The user-visible product is identical, which is exactly why “it works in the demo” tells you nothing about production-readiness.

The honest trade-off: getting to production-ready costs real time, and most of that time buys nothing a customer will ever see or thank you for. That is genuinely frustrating when a demo already looks done, and it is the single reason teams ship before they should. The trade is not optional for anything holding user data, but it is honest to admit the bill feels like pure overhead, right up until the 3am page or the GDPR letter makes it the cheapest money you ever spent. For a true throwaway prototype, skipping it is the correct call.

Wavect’s production-readiness pass is the bridge from a vibe-coded or AI-generated prototype to something you can charge for, run under Software Quality Assurance . We work the checklist in order of blast radius: authorization first, because that is where the worst bugs live, then validation, secrets, error handling, then the TDD regression suite and the CI/CD gate that keeps it green. The goal is not perfection. The goal is that nobody gets paged at 3am for something you could have read before you shipped.

When this matters in a software project. Right before real users arrive. Production-ready is the concrete line between software that works in a demo and software that works while you are asleep and a stranger is using it wrong.

What founders usually get wrong. They treat it as a feeling or a milestone to declare rather than a checklist to clear. Most of the work buys nothing a customer ever sees, which is exactly why teams ship before they should, right up until the 3am page makes it the cheapest money they spent.

How Wavect handles it. We work the checklist in order of blast radius, authorization first, then validation, secrets, error handling, then the regression suite and CI/CD gate. The QA checklist before launch and the vibe-coded prototype to production guide cover the full pass.

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FAQs

Concretely: authorization on every endpoint, validated input, no secrets in the client, fail-safe error handling, a regression suite that runs on every change, observability, and a fast rollback path. It is a checklist, not a feeling. A working demo proves none of these exist.
A demo only exercises the happy path you walk through. Production exercises the hostile path, the careless user, the curious user changing an ID in the URL, the unexpected input, the concurrent load. Production-readiness is precisely the work that the happy-path demo never touches and never reveals as missing.
It depends on how the prototype was built, but because the gaps in AI-generated code are systematic, the pass is usually faster than people fear. We work in order of blast radius (authorization, then validation, secrets, error handling, then tests and the CI gate) so the riskiest holes close first even if the budget runs short.