Technical Due Diligence for Vibe-Coded Startups: What Investors Actually Need to Check
AI is no longer just a tool that helps developers write individual pieces of code. In many startups, it now contributes to a significant part of the product-from backend logic and interfaces to integrations and infrastructure decisions. For investors, this changes the way technical due diligence should be approached.
Traditional due diligence still looks at architecture, testing, documentation, technical debt, and security. But with an AI-generated codebase, that is not enough.
It is also important to determine whether the product can be independently understood, maintained, deployed, and extended by an engineering team. The assessment should identify hidden dependencies, undocumented assumptions, architectural weaknesses, and platform-specific components that could increase the cost or complexity of future development and scaling.
That is why technical due diligence for AI startups should assess more than code quality alone. It should show whether the codebase is understandable to the team, under control, legally sound, and ready for further growth. These are often the areas where risks remain hidden from a standard technical review.
Why Vibe-Coded Codebases Need a Different Kind of Due Diligence
Traditional technical due diligence usually starts with familiar areas: architecture, code quality, test coverage, documentation, infrastructure, and technical debt. All of these still matter for products built with extensive use of AI. The problem is that they can show the state of the system without revealing how well the team actually understands and controls it.
In a vibe-coded startup, some architectural decisions may have emerged directly from conversations with an AI tool. The code works, but no one documented why a particular approach was chosen, which alternatives were considered, or what limitations were introduced along the way. In some cases, the team can maintain the product only as long as the person who remembers the context behind those decisions remains involved. For an investor, that is no longer just a code quality issue – it is a business risk tied to undocumented knowledge held by one or two people.
Another important question is whether the product that was actually built corresponds to the product the founder, customer, or investor intended to create. Vibe-coded applications can look surprisingly complete in a demo, with polished interfaces, multiple screens, and a long list of seemingly finished features. But visual completeness does not necessarily mean functional readiness.
Once reviewers walk through real end-to-end scenarios, they may discover that users do not know what to do next, the UI does not reflect the actual business process, page names and interface elements do not match the terminology used by the business, or individual features exist without being connected into a coherent workflow. Critical end-to-end use cases may be missing entirely. In other words, the application may demonstrate features without supporting the actual user journey.
Regulation adds another layer. The use of vibe coding does not automatically make a product a high-risk AI system. However, Article 6 of the EU AI Act classifies certain AI systems as high-risk depending on their intended purpose and area of use, including systems that fall under Annex I or Annex III. In such cases, additional requirements may apply to risk management, documentation, testing, and conformity assessment.
The cost of overlooked issues can also be significant. According to figures cited by EDUCBA, technical debt can increase post-acquisition costs by 30-50%, while more than 60% of surveyed M&A professionals reported that technology issues missed during diligence had a material impact on the outcome of a deal.
That is why reviewing a vibe-coded product should answer not only “Does the code work today?” but also the more important question: “Does the company understand what it owns, and can it develop it safely tomorrow?”
What Investors Actually Need to Check
When reviewing a vibe-coded startup, investors need to look beyond whether the product works and whether the team can ship features quickly. The key question is how resilient the business is once you remove the current pace of AI-assisted development from the equation and treat the codebase as an asset that needs to remain viable for years.
Code Ownership and IP
The first thing to verify is who legally owns the code and under what terms it was created. If the team used third-party AI services, coding assistants, app generators, or prebuilt components, investors should review their licensing terms, commercial – use restrictions, and the origin of critical parts of the codebase. A working product does not automatically mean that ownership rights are clear for every component it relies on.
Architecture and Scalability
The next question is what happens if traffic, customer numbers, or data volumes increase tenfold. AI tools can be effective at building an MVP quickly, but they often optimize for the immediate task rather than long-term architecture. Investors need to understand whether the existing system can scale incrementally or whether the next funding round will effectively finance an expensive rebuild.
Security Vulnerabilities
AI-generated code should be reviewed specifically for common security issues: API keys and secrets exposed in client-side code, insufficient input validation, weak authorization controls, unsecured endpoints, unverified webhooks, and outdated dependencies. The most dangerous problems are often found in code that looks correct and passes basic tests but fails to account for real-world attack scenarios.
Documentation and Team Knowledge
A strong review should assess not only whether documentation exists, but whether the team actually understands the system. What happens if the person who built most of the product through conversations with an AI tool leaves tomorrow? Can other engineers understand critical flows, deploy the system, resolve incidents, and safely modify core business logic? If essential knowledge exists only in one person’s memory or in old prompt histories, that becomes an operational risk.
Compliance
For fintech, healthtech, insurtech, and other regulated industries, compliance with data protection, security, auditability, and AI-related requirements should be assessed separately. Depending on the product’s purpose and market, regulations such as GDPR, industry-specific rules, or the EU AI Act may apply.
This is why technical debt due diligence investors should not be treated simply as a search for “bad code.” It is an assessment of future obligations: how much remediation may be required after the deal, how predictable those costs are, and whether hidden technical issues could materially change the economics of the investment.
What Investors Really Need to Check: A Practical Checklist
AI-Generated Code Due Diligence: A Practical Checklist
If the previous stage of due diligence identifies where the main risks are, this stage should show how to verify them in practice. Strong AI-generated code due diligence should rely not on the team’s assurances, but on verifiable evidence: repositories, change history, dependencies, tests, documentation, and deployment processes.
1. Establish What You Actually Own
The first question is not how the code was created, but whether you actually have access to the software you are buying.
Determine whether the application has a complete and accessible codebase or remains dependent on a closed AI/no-code platform. Check whether the source code can be exported, whether it can be built and deployed independently, and whether the application depends on proprietary platform services.
Also identify where the database, user accounts, files, authentication, integrations, and other critical components are hosted and who controls them.
A product that works today but cannot be independently developed, deployed, or migrated may carry significant platform and vendor lock-in risk.
2. Build a Dependency Inventory
Review not only third-party libraries and packages, but also the external services the application depends on. Identify the databases, authentication providers, file storage, email services, payment providers, e-signature platforms, APIs, and other SaaS components used by the product.
For each critical dependency, determine why it is being used, which parts of the application depend on it, whether it is appropriate for the product’s requirements, and what would happen if the service had to be replaced.
Also review package versions, licenses, abandoned libraries, known vulnerabilities, and dependencies that may have been introduced without proper evaluation. AI-generated applications can accumulate both unnecessary libraries and external services simply because they were convenient during development.
The goal is to understand not just what the application depends on, but whether these dependencies are intentional, appropriate, replaceable, and sustainable as the product grows.
3. Run Secrets and Security Scans
Repositories, frontend bundles, and configuration files should be checked for exposed API keys, tokens, credentials, and insecure settings. Authentication, authorization, webhooks, input validation, and public endpoints should also be tested separately.
4. Validate the Tests in Practice
Having a tests folder proves very little on its own. The test suite should be executed, critical flows should be reviewed for coverage, and the tests should be checked to ensure they can actually detect failures rather than simply create the appearance of coverage.
5. Analyze the Actual System Architecture
Do not rely on the founder or product owner to explain how the system works. In many vibe-coded startups, the person who created the application may have little or no software engineering background and may not know how the underlying architecture was implemented.
Instead, assess the architecture directly from the available source code, configuration, infrastructure, database, integrations, and deployed application. Map the main components, data flows, business logic, external services, and dependencies to determine how the system actually works.
The goal is to identify architectural weaknesses, tightly coupled components, unnecessary complexity, single points of failure, and dependencies that could make the product difficult to maintain, extend, or scale.
6. Verify Deployment and Operational Readiness
Assess how the application is actually built, configured, deployed, and operated. Determine whether the available source code and infrastructure configuration are sufficient to deploy the application outside the original AI Builder or development environment.
Check the build and deployment process, environment configuration, secrets, databases, file storage, external services, domain configuration, and other infrastructure components required to run the product.
The key question is whether the application can be reliably deployed and operated in the environment required by the business – and what additional engineering work would be needed to achieve that.
7. Define a Remediation Plan
The final output of AI-generated code due diligence should not be a list of issues. It should be a clear action plan: what must be fixed before the deal, what should be addressed before scaling, and what can remain as acceptable technical debt. This is what helps investors estimate not only the quality of the current product, but the real cost of developing it further.
AI-Generated Code Due Diligence: A Practical Checklist
The M&A Angle: Code Audits Before Acquisition
When acquiring a startup, technical risks stop being an internal issue for the team – once the deal closes, they become the buyer’s responsibility. That is why an independent codebase review is especially important before signing, while there is still time to understand the real scope of future obligations and factor them into the company’s valuation.
With an AI-generated product, the task becomes more complex. It is not enough for a buyer to confirm that the application works and serves current customers successfully. The review should independently assess whether the architecture is maintainable, whether there are critical security issues, whether the logic of key modules is understandable, whether dependencies are properly managed, and whether a new or expanded engineering team can continue developing the system without relying constantly on its original creators.
In practice, an M&A code audit AI-generated codebase should separate issues that can be fixed through routine refactoring from risks that could materially affect post-acquisition integration costs. One set of findings may mean a few weeks of planned engineering work; another may point to the need to replace a core component or partially redesign the architecture.
An independent code audit gives buyers and investors an external assessment from specialists who are not under pressure to present the product in the best possible light before the deal closes.
The goal of the audit is not to uncover as many flaws as possible. It is to give both sides an objective technical picture: which risks are truly significant, how much effort remediation may require, and which issues should be taken into account before the acquisition is completed.
How SCAND Approaches Due Diligence for Vibe-Coded Startups
At SCAND, we do not treat a vibe-coded product as a technical mistake that automatically needs to be fixed by rewriting it from scratch. For a startup, AI tools are often a rational way to validate an idea faster, launch an MVP, and get to market. What matters more to us is whether the system is ready for the company’s next stage of growth.
SCAND has more than 25 years of experience in software development, modernization, and code auditing, so we evaluate AI-generated code in the context of the entire system rather than in isolation. Our specialists can assess architecture, backend and frontend, infrastructure, security, dependencies, code quality, and development processes as a whole. This is especially important in due diligence, where a technical issue matters most when it creates financial, operational, or scalability risks for the business.
That experience also helps us distinguish truly critical limitations from ordinary technical debt that can be addressed gradually. Instead of defaulting to “rewrite everything,” the SCAND team identifies what can be preserved, what should be improved through refactoring, and which components may genuinely need to be replaced.
For founders who want to bring an AI-built product to a more mature engineering state before a funding round or the next stage of growth, SCAND also offers Post-Vibe-Coding Development – from technical assessment and codebase stabilization to further development and scaling.
Conclusion
AI-generated code is not a red flag by itself. The real question is whether the company understands what it has built, can maintain it without relying on a few individuals, and knows which technical risks could affect growth, investment, or acquisition. For founders, due diligence is an opportunity to identify those issues before they become part of the negotiation. For investors and buyers, it provides a clearer picture of the product behind the demo.
Frequently Asked Questions (FAQs)
Is AI-generated code automatically a red flag for investors?
No. Investors are usually more concerned with maintainability, security, ownership, scalability, and team knowledge than with how the code was produced. AI-generated code becomes a risk when the company cannot explain, support, or safely evolve the system.
How is due diligence different for a vibe-coded startup vs. a traditionally built one?
The core checks remain similar, but vibe-coded products require extra attention to code provenance, undocumented AI-driven decisions, dependency risks, security gaps, and knowledge concentration. Reviewers also need to verify whether the team can maintain the system without relying on one person or tool.
Does the EU AI Act apply to vibe-coded products?
Not simply because a product was built with AI tools. The EU AI Act applies based on the AI system’s purpose, functionality, and risk category. Some products may fall under high-risk requirements, while many others will not.
Can a startup prepare for technical due diligence before investors ask?
Yes. Founders can review architecture, security, dependencies, documentation, test coverage, IP ownership, and deployment processes in advance. An independent audit can also help identify issues early and prioritize remediation before they become part of an investment or acquisition discussion.
What’s the difference between a code audit and technical due diligence?
A code audit focuses primarily on the technical condition of the codebase, including quality, security, architecture, and maintainability. Technical due diligence is broader and connects those findings to business risks such as scalability, team dependency, compliance, future costs, and transaction readiness.
