From AI prototype to production: a checklist for founders and product leaders
Your Replit, Lovable or Bolt prototype proves the idea. Here’s what it takes to turn it into a secure, scalable product on AWS without a big cloud bill or a months-long rebuild.
AI app builders like Replit, Lovable and Bolt have changed how products get started. A founder or product manager can now click through a working version of their idea in an afternoon. That’s a real advantage. But a prototype that impresses investors isn’t the same thing as a product you can put real customer data into. This checklist covers what we look at when we take a prototype to production, based on projects like the AI hiring platform we built for Smartal.ai.
1. Treat the prototype as the spec, not the codebase
A clickable prototype is the best requirements document you’ll ever have. Every screen, button and flow is something your team has already agreed on. Keep it as the source of truth for the user experience, and let your engineers decide what the production architecture underneath it should look like. Trying to harden prototype code line by line usually takes longer than rebuilding the backend properly.
2. Find what the prototype gets away with
- Hard-coded or sample data that needs a real database and data model
- Missing or client-side-only authentication and authorization checks
- Long-running AI calls inside the UI that will time out or frustrate users
- Secrets and API keys stored where they shouldn’t be
- No logging, monitoring or error tracking
3. Go serverless so costs track usage
Early-stage traffic is spiky. A job post goes out, a launch hits, and then things go quiet. On AWS, services like Lambda, API Gateway, Cognito and Amplify let you pay for requests instead of servers that run all night for nobody. Set up AWS Budgets and alerts on day one, and keep heavy AI work out of the request path so users never wait on a slow call.
4. Capture data as fields, not free text
Prototypes tend to store everything as big text blocks. Production apps, and especially AI features like matching or search, work far better with structured, validated fields. Define each field’s rules once and enforce them in both the UI and the API, so nobody can work around them by skipping the front end. The same goes for business rules, like limiting a platform to US users.
5. Plan for big changes, because they’re coming
Once real users see early builds, priorities shift. On the Smartal.ai project, both main user flows changed significantly in week three. What kept the launch on track was a modular backend, where each capability lives in its own small service, and having the same engineer on the project from start to finish. A big change became a re-plan, not a restart.
6. Ship small and ship often
Set up separate dev and production environments and a CI/CD pipeline in the first week. Then release in small pieces so stakeholders see progress on a live URL every week, instead of waiting months for one big reveal.
Who should do the work?
This kind of project goes best with one senior engineer who owns the outcome, sits in your standups and can make architecture decisions with you in the room. That’s exactly what a Forward Deployed Engineer is for. Your team keeps designing in the prototype tool, and the FDE keeps shipping to production.