Audit for a project built with AI
We review AI-generated code before launch and on systems already in production — for stability, scale, and investor questions.
An AI-generated project audit is a review of architecture, security, and licenses in code from Copilot, Cursor, or ChatGPT — before launch and once the system already serves users.
Code from language models often holds up in a demo, then fails when traffic grows, a new developer joins, or an investor asks about security.
We review six layers: architectural consistency, security, data protection, further development, library licenses, and cloud operating cost.
We check whether this is one coherent design or disjointed fragments from prompts — patterns such as SOLID and DDD, or the lack of them. See what separates an AI prototype from a production product.
A deeper review than automated scanners. We look for OWASP-list issues and secrets baked into the code before attackers find them.
We check how the application processes personal data and where context leaks to models. Gaps expose users and block a later security audit. Guide: how to use OpenAI safely with company data.
We assess whether the code is readable and extendable. The goal: any senior developer can take over without a three-month onboarding or a full rewrite.
Models often pick libraries with conflicting licenses. We review dependencies so ownership is legally in order, without deprecated or unsafe packages.
We flag inefficient generator routines and queries that degrade the application and raise the AWS, Azure, or GCP bill.
The gap shows up when users grow, a security audit starts, and an investor asks questions. A fast start with models makes it easy to confuse a demo with a product — how to use AI on an MVP without parking debt in the architecture.
Prototype from AI tools
Ready for customers
After the audit you receive: an assessment for leadership, an architecture write-up, a security report, a prioritized remediation plan, a recorded walkthrough with an architect, and a funding-readiness summary.
We translate technical risk into business impact. You get a clear recommendation: continue, pause the launch, or fix the foundation first — including when the system is already live.
A review of structure, patterns, and code quality. Concrete findings for the engineering team — not generalities.
Issues found, with risk rating and how to remove them: from OWASP-list items to leaked credentials.
Not a bug list on its own. We split problems into critical, material, and minor, so you know what to fix first.
A recorded session: an architect walks through the conclusions and justifies each recommendation.
A short account of code state and risks that you can attach to investor materials.
The audit is for people who used AI to build a product and need confidence that the foundation will hold production load, a funding round, or a new team — before launch and when the system is already live.
Four stages: from an unclear code state to a plan you can execute.
We set the scale (for example 10,000 users), GDPR requirements, and a funding or launch date, so the audit measures what will actually block growth.
We combine a scan with a manual review of logic, dependencies, and system consistency — what a scanner alone will not catch.
We order findings by business impact. At high debt: repair, harden, or rebuild.
Report, recording, and an agreed next step: repair, prepare for launch, or a deliberate rewrite.
Senior engineers run the audit — 9 years of experience on average in the team. The report is usually ready in 3–5 business days from repository access.
Engineers and architects who connect a code review to a business decision: repair, harden, or rebuild.
Scope and depth follow your system and goal: launch, a system already in production, a funding round, or a new team taking over.
From read-only access to the report is usually 3–5 business days — depending on repository size.
Scanners find known patterns. An architect judges whether the architecture will hold growth, a new team, and investor questions.
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Our Commitments to YouYou can trust that your data is safe and secure with our ISO 27001 certification and best practices in security and data protection.
You get piece-of-mind with our QA processes that adhere to the highest standards for delivering enterprise-grade software products.
You can count on us to quickly adjust to changes in your project needs and provide engineering talent with the required skills.
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A code audit assesses quality, security, and maintainability of any codebase. An AI-generated project audit also looks for generator traces: inconsistent logic, opportunistic dependencies, secrets in code, and gaps that appear when the model optimizes only for a quick, working result.
If a team wrote the code without AI, a code audit is usually enough. If Copilot, Cursor, or ChatGPT built a large part of the application — this audit.
We usually deliver the report and remediation plan in 3–5 business days. Timing depends on repository size and architectural complexity.
We sign an NDA before we start. Read-only repository access is enough — we do not copy the code onto our environments or take any ownership rights.
Yes. There is always an assessment for leadership in plain language: what threatens the launch, the round, and maintenance cost.
The engineering team gets a separate section with concrete fixes. The recorded walkthrough explains why a given issue is critical.
Usually part of the solution can be kept. When repair cost exceeds a rebuild, we say so directly — with a comparison of repair versus rewrite.
A common mistake is treating a proof of concept as a finished product. The audit separates what stays from what you should build on purpose.
Yes. Investors ask about security, licenses, further development, and whether one developer “holds everything in their head.”
You get an independent assessment of code state and a funding-readiness summary — material for the conversation, not a certificate promise.
Most often JavaScript and TypeScript (React, Node.js, Next.js), Python (Django, FastAPI), and AWS infrastructure. We also review Azure and GCP when the environment is hosted there.
If an AI tool generated a project in that stack, our seniors will assess it.
A scanner is fast on known vulnerabilities and repeatable patterns. It will not judge whether the architecture will hold growth, whether the logic covers unusual business cases, or whether a new team can enter the code without a quarter of onboarding.
We combine automated scans with a manual review — a strategic assessment an algorithm will not make.
