Meet Maya. She's building a field-service platform and starts the way everyone does now — she describes it to Lovable, the AI codegen tool that turns a prompt into a real full-stack app she owns and can sync to GitHub. It's genuinely lovable, and it gets her a working demo fast. The wall isn't the code Lovable writes — it's everything after codegen: keeping a growing app correct as it changes, on the database of her choice, without re-earning it every time. Here's where she hits it, and what we do differently.
The wall
One prompt, a real working app — frontend, database, auth, deployed, and genuinely hers. But it's codegen: generated code with no schema as the source of truth. The screens agree because they were generated together once, not because a contract makes them agree. The first time it changes, that guarantee is gone.
What we do differently
We start from a typed schema — entities, relationships, constraints — before a single screen. The demo is backed by a real data layer from minute one, so what wins the room is the thing you'll actually ship.
The wall
Codegen tools are great at happy paths and quick to generate a login. The trap is the edges: protected routes and row-level rules that look right in the demo before they're actually hardened. It's easy to ship a screen whose permissions were never truly enforced — and only find out when someone sees a record they shouldn't.
What we do differently
Permissions are derived from the schema and generated — the same model enforced in the UI and again as an API guard. Row-level security and multi-tenant isolation aren't a prompt; they're a projection of the entities she already defined.
The wall
A customer asks for one new field. She re-prompts; the agent edits real code and burns credits, and now and then rewrites something she'd hand-fixed last week. Every change is a negotiation with a generator over code she now maintains. On complex apps the quality drifts and the iteration bill climbs.
What we do differently
She changes the schema and regenerates. The output is byte-reproducible — every projection moves together, nothing she didn't touch drifts. Regenerate, don't refactor. The app gets more solid with each change, not less.
The wall
The generated backend is tied to one provider's model. That's fine — until a rule doesn't map onto it, and the answer becomes “leave the tool and write it by hand.” The stack quietly picks your architecture for you, and heavy or unusual logic is where that choice starts to cost.
What we do differently
We generate onto the database you choose. We support Supabase too — but the engine targets any database, and business rules are isomorphic: written once, enforced in the browser and again on the API. Your architecture is a decision, not a default. See the stack →
The wall
A big customer wants audit trails, data residency, and compliance sign-off. Prototype-first tools add these late, and whatever doesn't come out of the box becomes her problem under a deadline — the last mile she thought she'd skipped.
What we do differently
Audit logs, evals, and rollout gates are a governance layer of the stack, and workloads route to HIPAA-ready, private-compatible infrastructure. The enterprise checklist is built in, not a second system she bolts on under deadline.
The wall
She owns the code — that part's real. But she also owns its upkeep forever: a generated codebase that's now a static artifact, kept current by hand or re-generated with drift and credits. Owning the output isn't the same as owning a way to keep producing it correctly.
What we do differently
She owns the specs, the deterministic pipeline, and the domain experts — a way to keep regenerating the business correctly, not just a snapshot of it. Runs on her cloud, forkable, no meter. When the value compounds, it compounds for her. Our ownership stance →
The lovable part isn't the wall — it's what's on the other side of it. Start where the prototype stops.