DreamLabs
DreamLabs
[Beyond Lovable]
Lovable ships a real React and Supabase app in days. We take it the rest of the way: hardened security, an architecture that holds, tests and CI, running on infrastructure you own.
Free migration audit[Why it stalls]
Of everything in this category, Lovable gives you the most to work with. It generates conventional React with Vite, Tailwind, and shadcn/ui, backs it with Supabase, and syncs the whole thing to your own GitHub repository. That last part matters more than it sounds: unlike closed builders, you are not asking for an export, you already have the code. Teams get to a working, styled, authenticated product in days, and we have watched founders raise money on Lovable builds.
Where it stops is underneath the interface. Lovable leans on Supabase's row-level security, which means the safety of your app is essentially the quality of a set of database policies plus whatever checks happen to live in your components. When AI writes those policies to make a feature work, they tend to be permissive — and the security incidents that have made news across this whole category come down to the same short list: tables readable by anonymous visitors, policies that assert nothing, service-role keys reaching the browser, edge functions with no authentication in front of them. A user does not need to be a hacker to find these; they need the browser's network tab.
The second wall is velocity. Page components grow past a thousand lines, the same logic ends up pasted across five screens, and there are no tests, so nothing tells you when a prompt quietly broke checkout. Add the platform coupling — preview hosting, credits consumed per iteration, an editor between you and your own app — and the tool that made you fast in month one is what slows you down in month six.
[How it works]
[01]
We clone your repository, open your Supabase project, and review both against the failure patterns this platform produces: policies that allow everything, tables exposed to anonymous access, privileged keys in the client bundle, edge functions without auth, validation that only happens in the browser. In parallel we map the code — what is real, what is duplicated, what is mocked. You get a prioritized findings list and a fixed-scope plan before anyone writes code.
[02]
First we close what the audit found. Every table gets a policy written from an explicit permission model rather than from whatever made the feature work, privileged keys are rotated out of the client, edge functions get authentication and input validation, and the rules that decide who can see what move to the server where users cannot reach around them. Each rule ships with a test that tries to break it.
[03]
Senior engineers restructure the generated code into an architecture that holds: shared components instead of duplicates, TypeScript models shared between the client and the database, one data-fetching pattern, clear module boundaries. AI accelerates the mechanical refactors and test coverage while the structural decisions stay with the engineers. CI/CD and separate environments go in alongside, so from here a regression is caught by the pipeline rather than by a customer.
[04]
We deploy to your own hosting with staging and production environments, error monitoring, and alerting, point your domain at it, and preserve or redirect URLs so search rankings follow. Then you get the repository, the infrastructure accounts, and documentation a new engineer can onboard from — and the Lovable subscription becomes optional rather than load-bearing.
[Where you land]
You stay in the ecosystem you are already in, which is why this is one of the fastest migrations we do: React with TypeScript, either on Vite or moved to Next.js when server rendering or SEO justifies it, talking to the same Supabase project you already run — now with a properly designed schema, tested policies, versioned migrations, and real backups. Hosting sits on Vercel, Cloudflare, or your own cloud account, with CI/CD, environments, and monitoring around it.
Nothing about your product changes for users. What changes is that the rules protecting your data are enforced where users cannot reach them, every deploy is reversible, and any React engineer you hire can read the codebase without a tour.
[Other platforms]
[Smaller engagements]
Migrating off FlutterFlow, Bubble, Webflow, or Airtable instead? See no-code migration →
[FAQs]
It is, and that is a genuine head start over closed builders. But owning code and having production code are different things. What you own is generated React with no tests, permissive database policies, logic duplicated across screens, and no CI, environments, or monitoring around it. The migration is not about obtaining the code; it is about making the code something a security review, a new engineer, and ten thousand users can all survive.
There is a specific checklist worth running, today, whether or not you hire us: are any tables readable or writable by anonymous users; do your row-level security policies actually assert something about the current user; is a service-role key present anywhere in the client bundle; do your edge functions authenticate their callers; and is any permission decision made only in the UI. In our experience most Lovable apps that have never had an engineering review fail at least one of these. All of them are fixable.
No, and usually you shouldn't. Supabase is Postgres with a good platform around it, and it scales well past the point most products reach. The problem was never Supabase; it was how the schema and policies were generated. We keep the project, design the schema properly, rewrite the policies against a real permission model, and add migrations and backups. We would only recommend moving if your product has a specific requirement Supabase does not serve.
Some teams keep it for sandboxed experiments, and its GitHub sync is two-way, but once the codebase has been restructured, prompting changes back into it tends to undo the structure you paid for. The better pattern — and the way our own team works — is to keep using AI inside the repository with tools like Claude Code or Cursor, where types, tests, and code review catch what the AI gets wrong.
Prototypes start at $6,000 and full MVPs at $15,000. Most run about 6 weeks and land around $20,000 all-in, toward the lower end when the app is small or the Supabase backend is in decent shape, because the product decisions, design, and data already exist. Urgent security findings are fixed in the first week rather than waiting for the full timeline. The audit gives you a fixed quote up front.
[Interested?]