Aguilar Dev audits and rebuilds AI-generated codebases. I pair senior engineering judgment with AI-assisted analysis to catch what shipped-fast code usually misses.
Four things that determine whether an app can actually grow with the business.
Clean, consistent, and free of the copy-paste sprawl, dead code, and inconsistent patterns that show up when an app was built across many disconnected AI sessions.
Can a new engineer actually work in this codebase, or does everything live in one founder's head with no documentation or consistent structure to follow.
Vulnerabilities in the AI-generated code itself, plus the third-party libraries and dependencies it pulls in: auth issues, exposed secrets, injection risk, and known CVEs.
Whether the architecture holds up past today's traffic. You get a specific answer for what breaks first once usage grows tenfold, not just a vague warning.
Do the tests exercise edge cases and failure paths, or just the happy path? A passing test suite that only checks the easy case gives you false confidence.
Patterns that show up again and again in codebases built fast with AI tools.
A real developer looked at the repo and went quiet.
Every new feature seems to break two old ones.
You're not sure what's actually protecting user data, or if anything is.
Due diligence, a security review, or a new hire is coming, and you're dreading it.
It works today at 50 users. Nobody's checked what happens at 5,000.
Three stages. You can stop after any one of them.
A full pass through the repo: architecture, data handling, auth, error paths, scaling limits, and test coverage. You get a prioritized findings report at the end.
A scoped engagement to fix what the audit flagged, starting with anything that could lose you data, users, or a deal.
Monthly retainer for teams still shipping fast with AI tools. I review architecture decisions and pull requests before bad patterns creep back in.
A representative finding from a real category of issue.
Flat and scoped. You know the number before anything starts.
Full findings report with severity ratings and a prioritized fix list.
Scoped fix engagement based on your audit findings, priced after scoping.
Architecture and PR review, monthly. Cancel anytime.
I spent the better part of a decade working inside large, mission-critical enterprise codebases, the kind of systems where a bad deploy does not just annoy a few users. It costs real money and takes other teams down with it. That meant years of on-call rotations, incident reviews, and being the one accountable when something broke in production, not just the person who wrote the code that broke it. Aguilar Dev applies that same rigor to every AI-built codebase it touches. You are not getting a junior developer running through a checklist. You are getting someone who has actually carried the pager.
Then you get a short report and some peace of mind. That happens, and it's a good outcome. You only move to a rework sprint if there's something actually worth fixing.
No. I work directly from the repository. If the original builder (human or AI tool) is available for context, that helps, but it's not required.
Most common web stacks: JavaScript/TypeScript, Python, and the databases and cloud platforms (AWS, Azure, GCP) they typically run on. Ask if you're not sure yours fits.
No. It's also a good fit for small teams or agencies that inherited an AI-built app from a client or an earlier hire and need to know what shape it's actually in.
Tell me about the codebase. I'll reply with next steps within one business day.