Full.CX

What Full.CX is

The name points at customer experience, but the product does something else entirely: it takes a product idea that exists as a sentence or two and turns it into written material an engineering group can build from.

The gap it names is one most teams recognise. Requirements arrive vague, developers fill the holes themselves, and the disagreement only surfaces once the work is done. Product managers, founders, and agile groups too small to employ an analyst are the intended readers.

What you can do with it

You describe what you want in ordinary language and receive back a bundle covering who the user is, what the features do, the situations they apply in, how each one will be tested, and how the data hangs together, which is a set that normally eats several days of writing.

Acceptance criteria carry the most weight here, since they are what turns a requirement into something testable, and they are what teams write last, write badly, or never write at all. Everything lands in a single shared space, so product and engineering read from the same reference instead of two.

Who it is for

Product managers who write requirements as part of the job are the obvious readers, along with founders who have no product function to hand the work to and end up writing it themselves.

Small agile teams that want engineering to accept what they are given rather than argue with it fit here, and agencies building software for clients can pass the same output across as the delivery document. Anyone who has watched a sprint end in a disagreement about what one sentence meant will recognise the situation.

What to watch out for

Generated criteria inherit whatever the description assumed. A paragraph of intent yields criteria covering what that paragraph considered, while the requirements nobody raised are exactly the ones that start the argument later.

A full specification set arriving in minutes looks finished. The danger is treating it as reviewed, since review is what catches the misunderstanding, and a document this complete makes skipping that step feel safe.

Data models built from a feature description are the weakest output, because any model has to agree with what the system already keeps; one that suits this feature and clashes with the existing schema is only found once implementation starts.

Personas describe a plausible user, not a real one, which is handy for exposing assumptions and is not research. The seller's own copy reads as enthusiastic to the point of saying little about limits, and no pricing could be read when the page was checked.