¶ Engineering review for AI-built software
Your AI-built app works. Let’s make it hold.
We review software built with Lovable, Bolt, Cursor and Claude Code before it meets real users. We find what will break, rank it by risk, and help your team keep shipping with AI tools without lowering the bar.
Contents
Three situations we help with
-
The prototype is about to meet real customers.
Lovable or Bolt got you to a working product in days. Before real data and payments flow through it, we check authentication, database access rules, exposed keys, dependencies and deployment. You get a prioritized list: what must be fixed before launch and what can wait.
Production readiness review -
Your team should build with AI tools.
Claude Code and Cursor make good engineers faster, and they also speed up unclear processes. We help you set up repositories, permissions, review rules and a pilot, so AI-written code meets the same standard as everything else you ship.
AI coding adoption -
The AI code has become the problem.
Every change breaks something else, nobody can explain the structure anymore, and the hosting bill keeps growing. We map what exists, stabilize the critical paths and decide with you what to refactor and what to rebuild.
AI code rescue
Method
How a review works
Three steps. After each one, you decide whether and how to continue.
- Intro call
30 minutes, free. You describe the project, and we tell you honestly whether we can help.
- Findings
We read the code, infrastructure and workflow and write up what we find, ranked by severity.
- Next steps
We fix it ourselves, work alongside your team, or hand over a plan. You decide.
Index
What we look at
Authentication and data access, secrets and API keys, dependencies, architecture, tests, deployment and hosting, running costs, and how your team works with AI agents.
- Authentication & data access
- Sign-up, sessions, roles, row level security
- Secrets & API keys
- What ships to the browser, what sits in the repo
- Dependencies
- Unknown, outdated or hallucinated packages
- Architecture
- Where logic lives and how parts depend on each other
- Tests
- Which critical paths are covered, and which are not
- Deployment & hosting
- Environments, migrations, rollbacks, backups
- Running costs
- Queries, polling, model calls and other cost leaks
- Team workflow
- How AI agents, reviews and permissions fit together
Journal
Field notes
Notes on what can go wrong when AI-built software goes to production, drawn from current engineering and security writing and from public discussions among founders and developers. Every claim links to its source.
- Production readiness checklist for AI-built apps Production readiness · 4 min
- Supabase RLS in Lovable and Bolt apps: mistakes to check AI code security · 4 min
- Refactor or rewrite? Deciding on an AI-generated codebase Code rescue · 5 min
¶ Intro call
Book a free intro call
30 minutes, free of charge. Tell us about your app or your team, and we will tell you honestly whether and how we can help. We reply within one business day.
- You describe the app or the team setup.
- We ask about the stack, the data and what is at stake.
- You get an honest view of whether a review, a rescue or a rollout makes sense.