Auditing AI-generated code: reliability before production

What is Auditing AI-powered apps and tools?

AI assistants (Copilot, Cursor, ChatGPT, Claude, Gemini, and others) have radically accelerated code generation and application prototyping. But speed is not synonymous with quality: AI-generated code often exhibits repetitive patterns, unintentional architectural decisions, unnecessary dependencies, vulnerabilities inherited from the training corpus, and a veneer of remediation that makes it difficult to visually detect errors. At Q2BSTUDIO we offer a specialized audit for AI-generated code, tailored to the specific weaknesses of this type of software.

Our analysis goes beyond standard quality metrics. We look specifically: logic that looks correct but fails in edge cases (the statistical model optimizes for the average case), apparent but incomplete validations, cosmetic error handling (try/catch that silences unresolved), hardcoded values that should be configurable, shallow security patterns (hash without salt, JWT without audience validation, parameterized SQL but with injection at another point), and premature or absent abstractions that hinder evolution.

We also evaluate architectural coherence: when multiple prompts generate different modules, it is common for style, conventions, and patterns to vary significantly within the same project. This inconsistency generates confusion in the equipment, makes maintenance difficult and produces subtle bugs due to contradictory assumptions between modules.

The audit includes: adapted static analysis (configuration of rules that prioritize typical AI generation defects), manual review of critical flows (authentication, payments, sensitive data), dependency assessment (obsolete, vulnerable or redundant packages suggested by the model), testing analysis (actual coverage, relevance of tests and detection of tautological tests that do not validate anything), and verification that the documentation (if any) reflects the actual behavior of the code.

The deliverable is a report with findings prioritized by risk, contextualized with the nature of the defect (human error vs. typical AI generation pattern) and with remediation recommendations. We include a specific section on invisible debt: code that works today but will collapse under slightly different requirements (structural fragility).

This service is especially relevant for: startups that accelerated with AI and need to validate before seeking investment, teams that integrated code generated without extensive review, companies that commissioned development from freelancers who used AI without supervision, and CTOs who need a second opinion on software built partially or entirely with assistants.

We don't demonize AI as a development tool — we use it ourselves — but we know that the code generated needs expert human oversight, especially when it's going to support users, data, and real money.

The service adapts to the degree of AI use in the project: from fully generated applications (vibecooding without review) to projects where AI partially assisted in specific modules. In each case, we calibrate the analysis based on the proportion of code generated and the level of oversight it received during development.