Built with AI. Not ready for real users yet — and that is normal. You have a working demo from Lovable, Cursor, Bolt, v0, or Replit. We finish incomplete features, close security gaps, run structured QA, and help you publish — without rebuilding from scratch.
Lovable · Cursor · Bolt · v0 · Replit · Finish · Secure · Test · Publish
30 minutes · Senior engineer · No commitment
Vibe coding is a legitimate fast way to start — and that is how most of our clients arrive. AI builders optimize for a happy-path demo: sign-up works, the main screen loads, and checkout succeeds once. They do not automatically harden for production traffic, abuse, or edge cases. Real users mistype passwords, refresh mid-payment, upload oversized files, and share deep links. Bots probe public APIs. That is when shortcuts surface: authentication that relies on client-side checks instead of server rules, database queries that load entire tables because pagination was never added, API keys shipped in frontend bundles, and errors that leak stack traces to the browser. Supabase and Firebase apps often ship with Row Level Security disabled or misconfigured — fine when you are the only tester, dangerous when one user can read another's data. Payment webhooks without signature verification let attackers fake successful charges. None of this appears in a five-minute demo. It appears on launch night. Our job is to close that gap while keeping the codebase you already invested in.
We close feature gaps AI left incomplete or half-working — password reset flows that never send email, admin panels with no access control, file uploads with no validation, onboarding steps that break on mobile, and integrations stubbed but not wired. You keep the product direction; we make the flows actually complete.
We fix auth and data handling: Row Level Security on Supabase, Firebase rules, session management, role checks on every sensitive route, and secrets moved out of client code. We address injection risks, IDOR (users accessing each other's records), and exposed service-role keys — the vulnerabilities AI tools routinely skip.
Structured QA on critical user flows, API endpoints, payments, and auth — aligned with our dedicated QA for AI-generated apps practice. Manual exploratory testing plus targeted automation on paths that must not break before launch.
Production deployment, environment configuration, error monitoring, logging, and app store submission where applicable. You get a documented handoff: what we changed, how to deploy, and what to watch after go-live.
Concrete issues we see in Lovable, Cursor, Bolt, v0, and Replit codebases before hardening.
Supabase or Postgres tables where any authenticated user can read or update another user's rows because RLS policies were never enabled or tested.
Stripe secret keys, OpenAI tokens, or Supabase service-role keys committed in client bundles or checked into Git — copyable from browser devtools.
Stripe or Paddle webhooks that process events without signature verification, allowing forged payment-success payloads.
Loops that query the database on every row — fine for ten test records, slow or timeout-prone under real traffic.
Public API routes and AI-backed endpoints without throttling — easy abuse vector and runaway usage costs.
500 errors returning stack traces, internal IDs, or SQL fragments that help attackers map your system.
Sign-up without email verification, file uploads with no size limits, or forms that appear to save but write nowhere.
At-a-glance scope, pricing tiers, delivery timelines, engineering tradeoffs, and decision criteria for technical founders and product leaders.
Software QA and technical audit services cover manual and automated testing, performance and security checks, and senior engineer code review — especially for AI-generated or inherited codebases before production launch.
Pricing depends on scope, integrations, and timeline. GreeLogix provides transparent fixed-milestone quotes after discovery, with flexible models for one-off audits, sprint retainers, or dedicated engineers.
Smoke audit: 1 week. Full regression cycle: ongoing per sprint. Deep code audit: 5–7 business days.
Compare delivery models, cost profiles, and engineering tradeoffs before committing resources.
| Option / Approach | Best When | Primary Tradeoff / Risk | Recommended Fit |
|---|---|---|---|
| Developer-only testing | Early prototype with no paying users yet | Developers tend to test happy paths; misses subtle regression side effects, UX edge cases, and cross-browser quirks. | Early pre-revenue prototypes |
| Crowdtesting platforms | One-off device coverage without domain context | Fragmented bug reports without codebase context, inconsistent reproducibility, and zero sprint continuity. | One-off device compatibility checks |
| Automated scanning only | Known stack with mature CI; catches syntax not workflow bugs | Catches syntax and linting issues but remains completely blind to workflow breakdowns and complex business logic errors. | Baseline CI static analysis |
| GreeLogix Pod | You need production release confidence, automated regression suites, and senior QA engineers embedded in your sprint. | Requires active product owner for weekly sprint reviews and scope alignment. | Production & Scaling (Recommended) |
Talk to a senior engineer — scope, timeline, and cost range in one 30-minute call. No sales script.
Most clients arrive with a repo or export from Lovable, Cursor, Bolt, v0, or Replit Agent — not a blank specification. We read the generated code, map what works, and harden what does not. Vibe coding got you to a demo; we get you to something you can put real users on.
If you built with Lovable, our vibe coding with Lovable page covers how we extend those projects. For Cursor-native codebases, see vibe coding with Cursor. This production service applies regardless of which tool you started with — the finish, secure, test, publish path is the same.
Not every founder needs a full build-out on day one. If you want a senior engineer's assessment before committing to fixes, start with our free AI audit or a pre-launch app review. When you are ready to implement, we scope finish, secure, test, and publish as fixed milestones — same team, same standards, no rebuild unless the stack is genuinely wrong for your goals.
Answers to the buyer questions we hear most before a project starts.
Yes. That is the most common starting point for this service. We connect to your existing GitHub repo or Lovable export, review what the AI generated, and take it from demo to production. You do not need to switch tools or restart the project.
We work from what exists today — the deployed app, repository, and environment variables you can share. Original prompts help for context, but they are not required. Senior engineers read the code, trace the data flows, and document gaps from there.
It depends on scope and how many integrations you have. A focused hardening pass on a small MVP often takes a few weeks; apps with payments, multi-tenant auth, and mobile clients take longer. We give you a scoped timeline after a free audit — not a generic estimate before we have seen the codebase.
We work with what is already there. The goal is to keep your UI, core flows, and business logic while fixing security holes, finishing half-built features, adding tests on critical paths, and preparing deployment. A full rewrite is only recommended if the stack is fundamentally wrong for your goals — and we will say that plainly if it applies.
A full custom build starts from requirements and architecture first. This service starts from a working AI-built demo and moves it to production readiness. It is faster and cheaper when the product direction is already validated. If you need net-new product design from scratch, our custom software and MVP development paths are a better fit.
Share your app URL or repo. A senior engineer flags production blockers and outlines the finish → secure → test → publish path.
You get milestones, timeline ranges, and pricing options — fixed-scope where possible, no surprise rebuild.
We work in your codebase: features completed, security fixed, QA on critical paths, staging deploy for your review.
Production deploy, monitoring setup, documentation, and optional ongoing support or QA retainer.
Share your Lovable, Cursor, or other AI-built project — we respond within one business day with audit next steps.
Validate AI-generated code, ship with confidence, and catch issues before launch.
Use our free calculators and planners — then book a strategy call when you are ready for a senior engineer review.
Case studies, guides, and free tools from the same engineering team.
The TEST step uses the same approach as our QA for AI-generated apps service. Built with a specific tool? Read how we work with Lovable projects and Cursor codebases. For testing-only help, see QA for AI-generated apps.
Start with a free audit — a senior engineer reviews your app, flags production blockers, and outlines what finish, secure, test, and publish would look like for your codebase. Not ready for a build-out? Request a focused review instead.
Also see our code review & technical audit for review-only engagements with a written report.