An AI-built prototype can look finished long before it is ready. It runs, it demos well, and it is tempting to put it straight in front of paying customers. The work that stands between a working prototype and a production-ready product is usually invisible in the demo — which is exactly why it gets missed.
Security and access control
Who can see and do what? Prototypes frequently ship with weak or absent authentication and no real authorisation model. Before real users arrive, access control has to be deliberate and tested, not assumed.
Data protection and privacy
Once real people use the product, it holds real information. That brings obligations around how data is stored, who can access it, how long it is kept and what happens if the product sends data to a model provider. These decisions are far cheaper to make early than to retrofit.
Secrets and environment management
API keys and credentials embedded in prototype code are a common and serious weakness. Production systems keep secrets out of the codebase and separate configuration from code across environments.
Testing and release practices
A demo is verified by clicking through it once. A product needs automated tests and a repeatable release process so that a change can be shipped — and reverted — with confidence rather than crossed fingers.
Observability, reliability and cost
In production you need to know when something breaks, why, and how to recover. For AI features that also means watching model latency and cost, which can move sharply with usage. Logging, monitoring and incident readiness turn surprises into manageable events.
A practical path
None of this means starting over. It means reviewing the prototype honestly against these areas, fixing what real customers and real data require, and sequencing the rest. Done well, the prototype becomes the foundation of a product you can stand behind.