Before any AppUnik engineer works on a client project, they go through our internal CCA-F certification. It is not a vendor certificate. We built it ourselves because we kept seeing the same problems come up on projects: features that shipped but did not move any number, AI systems that worked in the demo and broke in production, and handoffs where the new team had no idea why things were built the way they were.
CCA-F is our answer to that.
Starting with the metric, not the feature list
The first thing a CCA-F engineer does on a new project is identify the business number the work is supposed to move. Not the sprint backlog. The actual metric the client cares about. That number drives every technical decision from there.
In practice this means some things on the original brief do not get built because they do not move the metric. And some things that were never on the brief do get built because they clearly do. That is a different kind of engineering relationship than most clients are used to.
Knowing how AI actually fails
Claude-native means understanding how large language models fail in production, not just how to call the API. Where they produce confident wrong answers. Where retrieval breaks down under load. How to build evaluation loops that catch problems before users see them.
CCA-F engineers are tested on this. So when they build an AI feature, monitoring and fallback paths are there from the first sprint, not added after something goes wrong.
Staying accountable after launch
The Forward part of CCA-F means the engineer who designed the system stays responsible for it in production. There is no handoff where institutional knowledge disappears. If something breaks at 2am, the person who can fix it fastest is the person who built it.
That changes what gets tested before launch, what gets documented during the build, and what the client receives at the end. A system they can actually maintain, not one they have to reverse engineer.
