Is your AI-built app ready for production?
See what to review in security, database, backups, tests, monitoring, and deploy before putting real users on an AI-built app.
Engineering for AI-built products
Your prototype became a product. Now it has to hold up.
You validated your idea with Lovable, Bolt, Replit, Cursor, Claude, or another AI tool. Origammi steps in when the system starts taking on real users, data, and payments — to review what was built, fix risks, and create a safe base to keep evolving.
For products already validated, in operation, or about to take on real users.
It worked fast. Now every change feels risky.
Building the first version got easier. The challenge starts when the product has to keep working while taking on new users, data, integrations, and features.
AI fixes one thing and breaks another
The same problem keeps coming back, new regressions appear, and each attempt needs more context than the tool can hold onto.
You don't know if the data is protected
The system has login and a database, but nobody has reviewed permissions, access rules, secrets, or data exposure.
No one can fully explain how it works
Parts were generated at different times, with different patterns, with no consistent architecture or documentation.
The product already has users but still runs like a prototype
There's no monitoring, alerts, tested backups, separate environments, or a safe way to undo a release.
Every new feature costs more
Simple changes ripple through large parts of the code, cause side effects, and add to technical debt.
The only recommendation seems to be rewriting everything
You don't know if a rebuild is truly necessary or just the most convenient answer for whoever just arrived.
Origammi steps in at three moments.
Prepare for production
The MVP worked and is about to take on real users, payments, or data.
Rescue a product already in operation
Bugs, failures, unexpected costs, or risks started showing up after launch.
Build a base to keep evolving
The product works, but every change has become slower, less predictable, or riskier.
Less opinion, more evidence
Not every AI-built system needs to be redone.
We first identify what's working, where the risks are, and which parts can keep evolving as they are. The decision can be to keep, protect, refactor, replace one component, or rebuild — always grounded in the real product.
- 01
Keep
Leave it as is — the evidence doesn't point to relevant risk here.
- 02
Protect
Add controls (permissions, backups, tests) without changing the existing logic.
- 03
Refactor
Reorganize a specific part, preserving current behavior.
- 04
Replace
Swap out one specific component for a more mature solution, without rewriting the rest.
- 05
Rebuild
Rewrite from scratch — reserved for when the evidence shows it's genuinely the cheaper path.
Free assessment
Is your product ready to take on real responsibility?
Answer questions about users, data, payments, security, backups, monitoring, and maintenance. The result shows what most deserves attention before you keep growing.
This result is a self-assessment based on the answers provided. It does not replace a technical review of the product's code, infrastructure, and permissions.Technical review
Find out what can stay and what needs to change.
The Production X-Ray reviews the product's code, infrastructure, and critical flows. You get a clear view of the risks, priorities, and the right sequence to stabilize and evolve the system.
- 01
Authentication and authorization
- 02
Database and permissions
- 03
Secrets and environment variables
- 04
Payments, webhooks, and integrations
- 05
Backups and restoration
- 06
Deploy and rollback
Execution
We don't just hand you a list of problems.
When the product needs intervention, Origammi can also execute the priority fixes and leave a safer base for it to keep evolving. Scope is defined from the X-Ray or an initial triage — we don't promise everything fits in a single sprint.
- Fix permissions and data exposure
- Protect endpoints and integrations
- Create backups and test restoration
- Add logs and alerts
- Cover critical flows with tests
- Organize deploy and rollback
Who leads it
Experience to make the decisions AI didn't have the context to make.
Carlos França is a software engineer with more than 15 years of experience building products, platforms, and large-scale systems. At Origammi, he applies that experience to review quickly-built products, separate real risk from generic alarm, and find the smallest intervention that makes the system safer and more sustainable.
You don't just get an automated tool or a checklist. The product is reviewed by someone who has to take technical accountability for the recommendations.
Insights
Content for people who built fast and now need to build safely.
More code does not mean more delivery: where AI moves the engineering bottleneck
AI can speed up code production without shortening lead time. Learn where the engineering bottleneck migrates and what signals to investigate.