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Back QA · Playbook

92% of defects caught before launch — here's the pass that does it.

A dedicated QA phase across devices, edge cases and failure states, running before every deploy rather than after a user complains.

Most defects don't show up in a demo — they show up on a real device, on a slow connection, with a user doing something nobody scripted for. Our QA phase exists specifically to find those before launch, combining AI-generated edge-case tests with a manual, human walkthrough of every flow.

We publish these because most of what's written about software delivery and AI is either pure hype or pure cynicism — this is just an honest account of how we actually work.

We're deliberately not the studio that quotes you a number and disappears until the deadline. Expect short, regular check-ins, staging access from early on, and a straight answer any time something turns out to be harder — or easier — than it looked at the start. If a piece of this isn't the right fit for your team, we'll say so directly rather than stretching the engagement to fill a quarter.

How we build it

01

Generate edge cases

AI drafts the inputs and states a human would take hours to enumerate by hand.

02

Walk every flow manually

A person clicks through the product the way a real, occasionally careless user would.

03

Fix, then re-verify

Nothing ships until the same test that failed passes clean on a second pass.

Frequently asked

Yes — this reflects our real day-to-day process, not a marketing simplification. Ask us for specifics on any project and we'll walk you through it in as much detail as you want.

This is one of a small set of field notes we publish — get in touch and we're happy to talk through any of it in more depth, including specific numbers and examples.

The same principles apply regardless of size — a two-week landing page project gets the same review discipline as a multi-month platform build, just scaled to fit.

Who this is for

  • Founders and engineering leads thinking about how AI fits into their own delivery process
  • Anyone evaluating how we think about the work before reaching out
  • Teams comparing how different studios talk about AI versus how they actually use it

What you walk away with

  • An honest account of how the work actually happens, not a simplified pitch
  • Specific, concrete examples rather than abstractions

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