AI MVP → Production
Built your MVP with AI. Now what?
Lovable, Bolt, v0, and Cursor are great for getting to a working product fast. But after hundreds of prompts, most codebases carry security gaps, scattered business logic, and architectural decisions you'll have to undo later.
We run a fixed-price diagnostic. You find out exactly what you have before committing to anything.
What accumulates
The things that work fine in a demo and fail in production
Security gaps at the seams
AI tools write code that works for honest users. They don't think adversarially. Exposed API keys, missing auth checks, and unvalidated endpoints are standard output after hundreds of prompts.
Business logic in the wrong place
After 1,000 prompts, the rules that govern your product end up scattered: some in the database, some in the API, some inside React components. Changing one rule means hunting down every place it lives.
Architecture that can't scale
Early schema decisions compound. A structure that made sense at prompt 10 creates real friction at prompt 1,000, and each feature added on top makes the migration harder.
No tests, no coverage
AI tools write for the happy path. Edge cases, error states, unexpected inputs — untested. You find out what breaks when a real user hits it.
None of this means vibe coding was a mistake. It probably wasn't. What you built is a validated concept. Making it production-ready is a different job.
The service
Codebase Diagnostic
A fixed-price assessment by senior engineers. We actually read the code, not just run scanners, and deliver a written verdict with the reasoning and the numbers behind each path.
If you decide to continue with Rather Labs, the diagnostic cost is credited toward the project.
$2,000
Fixed price
5–7 days
Turnaround
What's included
Security review: auth, API exposure, data access patterns
Architecture assessment: structure, coupling, scalability risks
Data model review: schema design and migration complexity
Business logic audit: where your rules live and what breaks them
Test coverage gap analysis
Written verdict: keep / refactor / rebuild with effort estimates
Follow-up call to walk through findings
The verdict
Three honest outcomes. No upsell.
Keep
The architecture is sound. We document what's working and hand you a prioritized hardening checklist for the things to fix before you scale.
Refactor
The core is worth keeping, but specific areas need real engineering attention: security, business logic, infrastructure. We tell you exactly what, and scope the effort.
Rebuild
The architecture can't support where you're going. We tell you why, and give you a scoped plan with real numbers so you know what you're deciding.
How it works
From repo access to written verdict
01
Share your repo
Read access to the codebase and a 30-minute call to understand the product, the stack, and where you want to take it. No intake forms.
02
We dig in
Senior engineers go through security, architecture, data model, business logic, and test coverage. Automated scans first, then manual review. The manual part is where the real problems show up.
03
Written verdict
Keep, refactor, or rebuild. With the reasoning, the specific risks, and an effort estimate for each path. Something you can take to a co-founder, an investor, or another team.
Who this is for
You've validated the idea. Now you need to know what you have.
The diagnostic is designed for founders who built their MVP with AI tools, have real users or investors engaged, and need a clear technical picture before deciding what to do next.
You built with Lovable, Bolt, v0, Cursor, or a similar tool
You have users or are about to launch publicly
Investors are asking about the technical foundation
You're spending more time fighting the AI than shipping features
You want an honest answer before committing to a full build
Fixed price · 5–7 days
Know what you have before you commit to anything.
Share your repo. We go through it and send you a written verdict: keep, refactor, or rebuild, with the reasoning and the numbers for each path. If you continue with us, the $2,000 goes toward the project.