CopyCat: AI Copy That Partners Actually Understand

The problem: an app its users can't read

Porter's delivery partners live inside the partner app — orders, payments, penalties, support. But over 80% of them struggle with reading and comprehending the app's English interface. Many navigate by memorising the shapes of a few familiar words. When the copy gets abstract — support flows, task instructions, penalty explanations — comprehension collapses: wrong taps, stalled tasks, support tickets that a clearer sentence would have prevented.

The standard fix is a copywriting pass. But copy debt regenerates: every new feature ships new strings, written by whoever built it, in whatever tone they had that day. The problem isn't a batch of bad strings — it's that the org had no system for producing partner-legible language.

The reframe: don't rewrite the copy, systematise the voice

CopyCat is two layers, and the order matters.

Layer one — a persona-first tonal matrix. Before any AI, I built the framework: who is reading (new partner vs. experienced partner), in what moment (task-critical, money-related, support, celebration), and what tone each cell demands — direct instruction, reassurance, plain-language explanation. The matrix encodes the language partners actually use — words observed from partner conversations, not translated corporate English.

Layer two — AI tooling that enforces the matrix. A designer or PM uploads the screen they're working on; CopyCat reads the context, detects the intended tone from the matrix, and generates partner-friendly copy options that fit it. The AI isn't freestyling — it's operating inside the framework, which is what keeps the output consistent no matter who's asking.

Early versions produced generic, could-be-any-app copy — the classic LLM failure. The fix was doubling down on layer one: richer screen context, tone constraints from the matrix, and continuous validation of outputs with the designers using it. Simplifying language turned out to be a deeply contextual exercise — not shortening text, but aligning it with the reader's reality.

Proving it

The sharpest external validation: CopyCat won Porter's company-wide AI hackathon, and from there moved into the org's actual workflow — designers and PMs generating on-voice, partner-legible copy without waiting on a copywriter, and support/task flows getting rewritten through it. In our post-launch checks, comprehension accuracy on rewritten flows improved roughly 20%, and the design team's time-per-copy-rewrite dropped by about 40%.

What I carry forward

AI didn't solve the comprehension problem — the tonal matrix did. The AI made the matrix enforceable at scale. That ordering (framework first, model second) has become my default for every AI intervention since: the model is only as good as the system it's allowed to operate in.