How to turn a Lovable/v0 prototype into a production application

AI builders ship demos fast. Production needs auth, payments, secrets hygiene, observability, and an architecture that survives real users — here is the hardening path I use.

Production hardening for AI-built prototypes

Teams now arrive with a working Lovable, v0, Cursor, Replit, or FlutterFlow prototype and ask the same question: how do we put this in front of customers without waking up to a breach or a broken checkout?

The failure mode is predictable. The demo path skips identity hardening, stores secrets in the client bundle, treats payments as a happy-path button, and has no evaluation story for AI features. Real users and attackers both find those gaps.

My production path is: inventory trust boundaries, fix auth and authorization, move secrets server-side, make payments idempotent, add monitoring, then evaluate AI behavior before expanding autonomy. You keep the stack you started with when it can be hardened — rewrite only when the architecture cannot carry production risk.

If you want this done end-to-end, the Prototype → Production engagement is built for exactly this intake. Bring the repo; leave with a shippable system.

Take the next step

See the hardening practice page

Vibe-code rescue

Choose Fix an existing prototype

Start a brief

Example of production AI product engineering

FoCoCo case

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