CASE STUDY

The AI built it fast. The engineer made it survive launch.

What changed between an AI-generated first draft and the version that actually shipped. Same product, real engineer, [PLACEHOLDER — replace with real customer data].

Before and after, line by line.
[PLACEHOLDER COMPANY]
THE OUTCOME
1[PLACEHOLDER — what shipped]
2[PLACEHOLDER — what it fixed]
3[PLACEHOLDER — measurable result]
[PLACEHOLDER STAT]

[PLACEHOLDER — real outcome metric]

BEFORE / AFTER

What the engineer changed, and why

BEFORE — AI-GENERATED
[PLACEHOLDER — real AI-generated code sample from the customer's build]

[PLACEHOLDER — one line on why this was dangerous: e.g. no error handling, blocking call, unvalidated input.]

AFTER — ENGINEER-REVIEWED
[PLACEHOLDER — the engineer's replacement code, with the key changes visible]

[PLACEHOLDER — one line on why the change matters in production.]

THE OUTCOME

What shipped, what it fixed

[PLACEHOLDER — outcome summary: what launched, which production risks were eliminated, any measurable improvement (uptime, conversion, load time). Replace with real customer data before publishing.]

“

[PLACEHOLDER — real founder quote about the before/after. Do not fabricate.]

[PLACEHOLDER NAME] [PLACEHOLDER TITLE], [PLACEHOLDER COMPANY]

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