Architecture and backend
We separate what can stay from what must be rebuilt, then add the data model, API layer, and background jobs the prototype skipped.
MVP to production
Turn your AI-built prototype into production software
Lovable, Bolt, Cursor, and Replit get you a working demo fast. Paying customers need more: secure sign-in, permissions, payments, a backend that holds up, and a way to know when something breaks. We add that layer and keep what already works.
- Production readiness review before any larger build
- Security, QA, and monitoring are part of the scope
- 01 AI prototype
- 02 Technical audit
- 03 Architecture
- 04 Backend and integrations
- 05 Security and QA
- 06 Cloud and monitoring
- 07 Production-ready product
Why AI-generated MVPs break after launch
AI coding tools are good at screens and happy paths. They are weaker at the parts nobody sees in a demo: who may read which data, what happens when a payment fails, how the database copes with production traffic, and how you roll back a bad release.
A Production Readiness Review makes those gaps visible. It covers architecture, code quality, security, scalability, backend and integrations, deployment, and technical debt, and ends with a recommendation for the next phase.
What we add to your prototype
Sign-in, roles, and payments
Proper authentication, permissions checked on the server, and payment flows that handle failures, refunds, and subscriptions.
Security and QA
A security review, automated tests on the paths customers use most, and fixes for the gaps that appear once more than the founder is using the product.
Cloud and monitoring
Staging and production environments, CI/CD, logs, alerts, and backups, so you can deploy, watch, and roll back with confidence.
From prototype to production in five steps
You see the findings and a recommendation before any larger build starts.
- 01
Readiness review
We read the code and use the app the way a customer and an attacker would.
Outcome Gaps ranked by launch risk
- 02
Architecture plan
What stays, what gets refactored, what gets rebuilt, and why.
Outcome Target architecture and estimate
- 03
Backend and integrations
Data model, APIs, auth, payments, and third-party services built to production standards.
Outcome A backend you can trust with customer data
- 04
Security and QA
Security fixes, automated tests, and a manual pass over critical flows.
Outcome A release candidate
- 05
Launch and monitoring
Cloud setup, CI/CD, alerts, and a rollback plan.
Outcome A launch you can observe
AI prototype vs production-ready product
The prototype proves the idea. This is what changes before customers depend on it.
| Area | Typical AI-built prototype | Production-ready product |
|---|---|---|
| Access control | Basic sign-in, rules enforced in the browser | Role-based permissions checked on the server |
| Data | Schema generated for the demo | Data model designed for growth, with migrations and backups |
| Payments | Test-mode checkout | Failures, refunds, webhooks, and subscriptions handled |
| Testing | Manual clicking before a demo | Automated tests on critical flows |
| Deployment | One environment, manual deploys | Staging, CI/CD, and rollback |
| Monitoring | Customers report the bugs | Logs, alerts, and uptime checks |
Related work
Frequently asked questions
Can you work with a product built in Lovable, Bolt, Cursor, or Replit?
Yes. We review the generated code, decide what can stay, what needs refactoring, and what is missing for production use, and plan the work from there.
Do we have to rewrite everything?
No. The review shows which parts are safe to keep and which will fail once paying users, payments, and permissions arrive. We rewrite only what the current code cannot carry.
What is a Production Readiness Review?
A focused technical assessment of architecture, code, security, scalability, backend, deployment, and technical debt. It ends with a prioritized list of gaps and a recommended next phase.
Can we keep using AI coding tools after you take over?
Yes. AI-assisted development speeds up iteration. Architecture, code review, security, and testing stay with experienced engineers.
Can we start with a technical audit?
Yes. For an AI-generated prototype, a focused technical assessment is the right first step before a larger build.