AI code generator

CleenUIvs.Lovable

Lovable generates full-stack web apps from natural language prompts; CleenUI is a licensed full-stack codebase you extend with AI tools like Lovable.

Where they fit

Lovable (formerly GPT Engineer) generates a new React + Supabase application from a natural-language description. It's fast for prototyping and impressive for demos. The tradeoff: every Lovable project starts from zero. There are no pre-built modules for auth beyond simple sign-in, no multi-tenant data isolation, no RBAC, no audit history, no background job infrastructure, no notification engine, no real-time layer. AI-generated code accumulates architectural debt in proportion to how much of the foundation it had to invent. CleenUI is the foundation AI tools like Lovable are good at extending — 14 vertical-slice modules, 524 documented endpoints, 300+ tables, 700+ stored procedures — already production-tested, already structured in a way that AI agents can read and extend without reinventing it.

Side by side

CategoryCleenUILovable
Runtime.NET 8 + React 18 (separate API + SPA)React + Supabase (PostgreSQL), generated per project
BackendASP.NET Core 8 Web API — 524 documented endpoints across 25 functional groupingsSupabase auto-generated schema + Edge Functions; no documented API surface
DatabaseAzure SQL — 300+ tables, 700+ stored procedures, Dapper + ADO.NET (no ORM)Supabase (PostgreSQL) — AI-generated schema, no stored procedures
Modules included14 vertical-slice production modules (M01–M14)None pre-built — AI generates from your description on every project
AuthAuth0 + JWT + RBAC + ABAC + row-level isolation + API-key auth (M01)Supabase Auth — sign-in/sign-up; RBAC and multi-tenant isolation are your problem
Multi-tenant isolationBuilt-in account / company hierarchy with row-level data scopingNot included — AI will attempt it if you describe it; production correctness not guaranteed
Background processing12-project .NET solution: 3 Azure Functions + 2 WebJobs + 6 shared librariesNone included
DistributionCustom-scoped engagement (one-time license, architect-led)SaaS subscription — free tier + paid plans; you deploy Lovable-hosted or export code

What CleenUI ships that Lovable doesn't

These capabilities are pre-built in CleenUI. Lovable has no equivalent — you'd build them from scratch or wire a third-party integration.

Multi-tenant data isolation

Row-level data scoping across all 300+ tables — every query is account-scoped at the data layer, not the application layer. Lovable generates isolation logic on request; production correctness under concurrent writes and schema migrations is yours to validate.

RBAC on 524 endpoints

Role-based access control enforced at the API layer across every endpoint — not a permission system you describe to an AI and hope it gets right. M01 ships the full auth + authorization stack.

Audit history

M04 Observability — structured audit trail across all modules. Lovable has no equivalent; you'd need to instrument every generated mutation yourself.

700+ stored procedures

A production-tested Azure SQL data layer with explicit, named stored procedures — auditable, indexable, and readable by your DBA. Lovable generates ad-hoc SQL via Supabase client calls; there's no stored-procedure layer.

Background job infrastructure

3 Azure Functions + 2 WebJobs with domain-specific workers (translation queue, media safety, video encoding, thumbnail generation). Lovable ships no async processing layer.

SignalR real-time

M07 Messaging: conversations, participants, presence, reactions, read receipts, draft state, media attachments — pre-built end to end. Lovable has no real-time layer.

When to pick which

Pick Lovable

Pick Lovable when you need a visual prototype or MVP quickly and the production architecture doesn't matter yet. It's well-suited for validating a product idea with non-technical stakeholders before committing to an engineering approach.

Pick CleenUI

Pick CleenUI when you're building production-grade B2B SaaS. The 14 modules CleenUI ships — auth, RBAC, multi-tenant isolation, background jobs, audit history, notifications, AI operations — are exactly the things AI code generators can't produce for you at production quality.

What they share

  • Both use React for the frontend
  • Both are used with AI coding tools to extend features
  • Both target web application delivery