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ENGINEERING · AI CODE FIXES

AI code audit and fix for vibe-coded apps

An AI code audit is a senior engineer's review of an app built with AI coding tools. We find the security, data and scale problems they left behind and fix them in your codebase, for a founder's prototype or a team's AI-written code.

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Led by Thanos GkanasCo-Founder, engineering

The drawing shows an orders screen and, under it, the browser sending an admin key to the orders table, which has no row policy, and getting other users' rows.

You built fast with an AI tool, and the app works. A vibe code audit answers the next question: is it safe to put real users and real data on it? We read the code, the database rules and the deploy setup, rank what we find, and then fix it with you. It is part of our startup IT consulting work, built for founders who want a straight answer before a launch, a raise or a growth push.

01SOUND FAMILIAR?

Signs your AI-built app needs a senior review

AI tools get an app working fast. These signs say it now needs a senior read.

  • SYMPTOM 01

    It works for me, but I am nervous about sharing the link

    Usually means: No one has checked what a logged-out stranger can read or change.

  • SYMPTOM 02

    Every new feature breaks an old one

    Usually means: The code has no tests and no clear structure, so each prompt patches a symptom.

  • SYMPTOM 03

    The AI keeps going in circles on the same bug

    Usually means: The real cause sits outside the file it is editing, often in the data or the state.

  • SYMPTOM 04

    I found API keys in the front-end code

    Usually means: Secrets are exposed. They need rotating, then moving behind a server.

  • SYMPTOM 05

    We are about to raise or sign a big customer

    Usually means: Someone will ask how the app protects data, and you need the answer in writing.

  • SYMPTOM 06

    It slows down once more than a few people use it

    Usually means: Queries and loops that were fine with test data are now on the hot path.

Led by Thanos GkanasCo-Founder, engineering

Not sure how risky your app is? Tell us what it does and what worries you, and we will say where we would look first.

Tell us about your project (opens our project form in a new tab)

02WHAT WE DO

What our AI code audit covers

Every vibe code audit runs in the same order: what can leak, what can break, what will not scale. Then we fix it in your repository, in changes you can review.

  • Access control and data exposure review

    We test what each type of user, and a logged-out visitor, can read and write. That covers database rules such as Supabase row-level security, API routes, file storage and admin screens. AI tools often enforce access only in the interface, so we check the server side every time.

  • Secrets, keys and dependency check

    We look for API keys and tokens in front-end code, logs and the repository history, and list which ones to rotate. We also check the packages the AI pulled in for known vulnerabilities and abandoned projects. For the background on why this matters now, read Vibe coding security: why AI-built apps exposed data in 2026.

  • Code structure and technical debt map

    We map where the logic lives, where it is duplicated and which files every change has to touch. You get a ranked list of where to start, and our guide on how to reduce technical debt explains the method behind the ranking.

  • Performance and scale check

    We run the app against realistic data volumes and look for slow queries, missing indexes, chatty API calls and work repeated on every request. The goal is to know where the first bottleneck sits before your users find it.

  • A ranked findings report you can act on

    Each finding has the evidence, the risk in plain words, the fix and a rough effort. It reads the same for a founder and an engineer, so you can decide what to fix now and what can wait.

  • Hands-on fixes in your codebase

    We fix the high-risk findings ourselves, in small reviewed changes with tests, so nothing is rewritten behind your back. If the audit shows the whole system needs attention, we say so and scope that work separately.

export async function listOrders(session) {Removed:   const db = connect(ADMIN_KEY);Removed:   return db.table("orders").all();Added:   const db = connect(PUBLIC_KEY, { session });Added:   return db.table("orders")Added:     .where("owner", session.userId)Added:     .limit(50);}Added: create policy owner_read on orders for select using (owner = auth.uid());

checks passing

Illustration of a fix that swaps the admin key for owner-only reads

03IN BRIEF

Why AI-built apps need their own code audit

AI-built apps need an audit that starts where the AI tools stop: access rules, secrets, data integrity and failure paths. Coding assistants are good at making a feature work for the person testing it. They are far less reliable at the parts nobody sees in a demo. Think of what a second account can read, what happens when a payment webhook fails, or which key ended up in the browser. A general code review reads for style and structure. An AI code audit reads for the assumptions the generator made and never checked. That is why we test the running app as well as reading the code, and why the first pass always covers who can see what. If you are still deciding whether building this way was sound at all, our guide "Is vibe coding bad?" covers the trade-offs in plain terms.

What you get from a vibe code audit

A vibe code audit ends with a written report and a fix plan, not a slide deck. The report lists every finding with its evidence, a severity, the business risk in one sentence and the change that removes it. The fix plan puts those changes in order: what to close before you share the app, what to fix before you scale, and what can wait. We walk you through it on a call, answer your questions and agree what we fix and what your team takes on. If the app is in good shape, the report says that too, and you keep the checklist for future releases. Before you commit to anything, try the vibe coding risk check. It gives you a quick read on how exposed your app might be, without sharing any code.

04STEP BY STEP

How the audit and fix works

  1. 01

    Audit

    We get read access to the repository, the database settings and the hosting account, then review the code and test the running app. You get the ranked findings report and a fix plan.

  2. 02

    Build

    We fix the high-risk findings in small, reviewed changes with tests, starting with anything that exposes data. You see every change before it merges.

  3. 03

    Ship

    We deploy the fixes, rotate any exposed keys and hand over notes on what changed, plus the checks that keep it fixed when you prompt the next feature.

05ENGAGEMENT OPTIONS

Ways to work with us on AI-built code

  • Working sessions

    Bring the app and your questions, and we review the riskiest parts with you, live.

    You get

    • A screen-share review of access rules and secrets
    • A written list of what to fix first
    • Answers your builder can act on

    Typical shape

    Thanos and you, one or more working sessions

    Quoted after a short call

    Ask for a quote

  • Audit and fix

    We run the full audit, then fix what matters most.

    You get

    • Ranked findings report and fix plan
    • High-risk fixes, with tests
    • Key rotation and deploy checks
    • Handover notes for your team

    Typical shape

    Thanos leads, end to end

    Quoted after a short call

    Ask for a quote

  • Ongoing review

    A senior engineer stays on to keep AI-written code safe while you build.

    You get

    • Review of AI-generated changes before they merge
    • Fixes and refactors as the product grows
    • A release checklist and tests on risky paths

    Typical shape

    A few days a month, reviewed monthly

    Quoted after a short call

    Ask for a quote

06WHO IT IS FOR

Who an AI code audit is for

  • Non-technical founders with an AI-built app

    You built the product yourself with an AI tool and want someone to tell you, plainly, whether it is safe to launch.

  • Technical founders who shipped fast

    You used AI to move quickly and want a second senior opinion before the code becomes your foundation.

  • Teams merging AI-written code

    Your engineers use coding assistants every day, and you want a review habit that catches what the assistant misses.

Thanos Gkanas

07LED BY

Who reviews and fixes your code

Thanos Gkanas leads engineering at BrainStormers, including AI code fixes, crisis response and infrastructure. He reviews the findings himself and stays on the fixes until they ship.

AI tools write code that works on the happy path. I read for the paths nobody tried: the logged-out visitor, the second account, the failed payment.

Thanos GkanasCO-FOUNDER · ENGINEERING
Thanos Gkanas
Co-Founder & Head of Engineering
Thanos's profileLinkedIn

08

Related engineering services

If the problem is already live and customers feel it, start with production incident response. If you would rather rebuild the core on solid ground, our MVP development services start with tests and access rules.

  • Code audit

    An independent source code audit of security, architecture, tests and delivery risk, written up as a ranked fix list.

    Thanos

  • Crisis response

    Senior help when production is down, a release failed or a vendor vanished: stabilize first, then fix the cause.

    Thanos

  • MVP development

    Scope the smallest product that proves demand, then build it with senior engineers on code you can grow.

    Thanos

  • Product and engineering

    Need product help as well? We can plan the fixes and the roadmap together.

    Roger and Thanos

09

Insights on AI-built apps

  • AI CODE FIXINGGUIDE

    Is vibe coding bad? What breaks when AI-written code reaches users

    Vibe coding is fast until real users arrive. What breaks when AI-written code ships, which apps are fine to build this way and the checks that catch problems early.

    Thanos Gkanas

  • AI code risk check

    Ten questions that show where an AI-built app is most likely to break: security, data, payments or operations.

    10 QUESTIONS · ABOUT 2 MIN

10FAQ

AI code audit questions

What is an AI code audit?

An AI code audit is a senior engineer's review of an app built with AI coding tools. It checks access control, exposed secrets, data handling, dependencies and performance, then ranks each finding by risk. The result is a written report and a fix plan, so you know what to repair before real users and data arrive.

Which AI coding tools do you work with?

We work with apps built in Lovable, Cursor, Replit and Claude, and with code from other AI coding assistants. The tool matters less than what it produced: we review the repository, the database rules and the hosting setup the same way. If the app lives inside a builder, we first make sure the code sits in a repository you own.

What problems do you most often find in vibe-coded apps?

The usual problems are database tables without access rules, API keys in front-end code and permission checks done only in the browser. We also often find no tests, duplicated logic and queries that slow down as data grows. Broken access control also sits first on the OWASP Top 10, the standard list of web application risks.

Should I fix my AI-built app or rewrite it?

Fix it if the data model is sound and the problems sit in specific places: access rules, secrets, slow queries, missing tests. Rewrite parts of it if every change breaks something else or the data model cannot support what you sell. The audit tells you which case you are in, with the reasons written down.

Is an automated AI code review tool enough?

Automated review tools are worth running and catch known patterns quickly. They do not know your business rules, so they miss problems such as one customer reading another's data through a valid request. If you are comparing the leading services providing automated AI code audits, treat them as a first filter, then have a person test access and data paths.

Do I need a code audit before scaling an AI-built app?

Yes, if more users, more data or a large customer is coming. Scaling multiplies whatever is already wrong: an exposed table leaks more records, and a slow query slows more people. Fixing it before growth is calmer than fixing it during an incident. For larger or older codebases, a full code security audit covers the rest.

Can my team keep building with AI tools after the fix?

Yes. We do not ask you to stop using AI tools. We leave a short set of rules for prompts and reviews, tests on the risky paths and checks in your deploy pipeline. New AI-written code then has to pass the same bar. Your builder keeps the speed, and the guardrails catch the regressions.

Do you need access to production data to audit the app?

Usually not. We need the code, the database schema and access rules, and the hosting configuration, and we test against a copy with fake or masked data where we can. If a problem only shows with production data, we ask first, use read-only access, look only at what the check needs and remove the access afterwards.

Led by Thanos GkanasCo-Founder, engineering

Talk to Thanos about AI code fixes

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NEXT STEP

Get your app fixed

Book a 20-minute call and bring a link to the app and two lines on what worries you. We will tell you what we would check first.

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