Your prototype proved the idea. We get it to production.
You got further with Lovable, Cursor, Bolt, or your own vibe-coding setup than most teams get in a quarter. That momentum is real. Production is a different job: auth, data boundaries, deploys, error handling, and support have to hold up when strangers depend on the app. We build with the same AI tools you use. The difference is convergence: we already know what production requires, so we get there in fewer rounds instead of spending your budget rediscovering it. Engagements start at $4k.
Expected outcomes
- A production readiness map with every part of the prototype sorted: ship, harden, or rebuild.
- A hardening plan for architecture, security, data, tests, and deployment.
- Launch sequencing for auth, data, payments, integrations, monitoring, and backups.
- A handoff plan for Lovable, Bolt, Cursor, Replit, v0, Bubble, or a custom stack.
- A fixed price for the work before it starts.
Best fit
Founders with a working demo
You prompted your way to something clickable, maybe even impressive. Now you need to know whether the architecture, data model, and deployment path are ready for customers.
Founders whose app is getting fragile
The demo worked, but every new feature now creates regressions, confusing states, or support questions you cannot confidently answer. Progress feels like circling.
Teams inheriting AI-generated code
You need senior product engineers to evaluate what is safe, what is risky, and what needs work before launch or the next hire.
What usually breaks
The happy path is overbuilt and the edge cases are missing
AI tools are good at producing visible functionality. They are less reliable at finding the awkward states users hit in production.
Data and permissions are too loose
Early prototypes often blur user boundaries, admin access, private records, API keys, and integration credentials.
There is no reliable change path
Without tests, deployment discipline, and clear structure, every improvement becomes a fresh risk, and bugs show up in places you did not touch.
The launch path is improvised
Hosting, environments, monitoring, backups, migrations, and rollback need decisions before real users depend on the system.
The app-builder handoff is unclear
Lovable, Bolt, Cursor, Replit, v0, and Bubble projects need a plan for source control, environments, database access, secrets, deployment, and ongoing ownership.
Every prototype gets sorted: ship, harden, or rebuild.
Not every prototype should be thrown away, and not every one should be patched. We make the call on evidence, part by part, and every piece of the system gets one of three verdicts.
This part already holds up, so it goes to production as it is.
The idea is right but the edges are not, so we keep this part and fix the auth, data boundaries, and error handling before users depend on it.
Fixing this part would cost more than redoing it, so we rebuild it properly and keep everything the prototype already proved.
The four checks every AI-built app gets.
Veracode found 45% of AI-generated code contains OWASP Top-10 vulnerabilities. So every audit runs the same four checks, and we report the results in plain language, not a scanner dump.
Can a stranger talk your app into doing things it should not?
If the app has an AI feature, user input, uploaded files, and pasted links can carry hidden instructions. We test whether any of them can push the app off script.
Can user A see user B's data?
We check every place the app stores and fetches data to make sure one customer's records never show up in another customer's account.
What is your AI allowed to touch?
If the AI can send email, change records, or call other services, it should only be able to do what you would trust a brand new employee to do unsupervised. We scope it that way.
What happens when the AI is wrong?
Models make things up. We make sure a wrong answer cannot corrupt your data, mislead your users, or trigger an action nobody approved.
How we help
Audit the prototype
We review the user flows, codebase, data model, integrations, deployment setup, security exposure, and support risks. You get a readiness map, not a sales pitch.
Sort it: ship, harden, or rebuild
The audit ends with a written triage verdict for each part of the system, in plain language, before any build work is scoped or priced.
Harden and ship
We do the cleanup and hardening, build the missing product surfaces, prepare deployment, and stay on after launch so the people who made the tradeoffs keep supporting them.
Send us what you have. We will tell you where it stands.
Share the GitHub repo, the Lovable or Bolt export, or just a link to the running app. We read the actual code before we quote, and the estimate you get back is fixed: the number we quote is the number you pay.
What works for us
- A GitHub, GitLab, or Bitbucket repo. Read-only access is fine.
- An export or share link from Lovable, Bolt, Replit, v0, or any other builder.
- A link to the deployed app if the code is not handy yet.
Common questions before the first call.
Can LOJI work with a prototype built in an AI app builder?
Yes. We work with prototypes from Lovable, Bolt, Cursor, Replit, v0, Bubble, and similar tools all the time. Bring the repo or export and we will tell you what the next step is.
Can a Lovable app work in production?
Sometimes. A Lovable app can be a strong starting point, but production readiness depends on the data model, auth, permissions, deployment, observability, and how much generated code needs cleanup before real users depend on it.
What should be checked before taking an AI-generated app to production?
Auth, authorization, data ownership, environment variables, API keys, database migrations, backups, monitoring, error handling, payments, admin access, and the workflows users will depend on first.
Can you clean up the code without stopping feature work?
Usually, yes. We sequence the work so the most exposed areas get stabilized first while the roadmap stays honest about what can safely ship.
How much does this cost?
Engagements start at $4k, which covers a focused audit and the highest-risk fixes. Larger hardening or build work gets a fixed price after the audit, so there is no open-ended meter running.
Keep moving through the AI launch system.
AI app security review
Review LLM and agent features for prompt injection, data leakage, tool permissions, and output validation.
MVP has users. Now what?
Move from feature shipping to product maturity, support, analytics, and repeatable adoption.
MVP from idea to market
Turn an idea, workflow, design, or AI-assisted concept into a focused first product.
Find out what your prototype needs before users depend on it.
Send the prototype, repo or export, stack notes, and launch goal. We will name what stands between the demo and a product people can rely on, and put a fixed price on closing the gap.