How do you run technical due diligence on an AI-built product?
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Our take
Treat an AI-built product like any acquisition, then add AI-specific checks. Assess code quality, security, scalability, and IP ownership as usual, but pay extra attention to whether anyone understands the codebase, whether it has tests, and who actually owns AI-generated code. You can run this yourself, but a fractional CTO does it faster and catches what an untrained eye misses. The biggest risk isn't bad code; it's a product no one can safely maintain or legally claim.
When a product was built largely with AI tools, standard due diligence still applies, but the failure modes shift. You’re no longer only asking “is this well built,” you’re asking “can this team keep building it, and do they truly own it.” Those two questions change valuations, and they’re exactly what an experienced technical leader is trained to surface.
The standard layer
- Code quality and structure: Is it readable and maintainable, or a tangle only its author navigates?
- Security: Authentication, data protection, exposed secrets, and known vulnerabilities.
- Scalability: Will the architecture hold at the growth the deal assumes?
- Test coverage: Is there a safety net, or does every change risk a regression?
The AI-specific layer
- Codebase comprehension: Ask the team to explain how core parts work. If the app was vibe-coded, they may not know, and that’s a maintenance risk you’re buying.
- IP ownership: Confirm who owns AI-generated code and that the tools’ terms permit commercial use. Ownership of AI-assisted output is still unsettled ground; get it in writing.
- Tool and dependency lock-in: Is the product tied to a specific AI platform in a way that’s costly to unwind?
- Documentation: Does anything explain the system, or does the knowledge live in one founder’s head?
Why a fractional CTO does it better
You can work through this checklist yourself, but a fractional CTO brings pattern recognition from auditing many codebases: they know which AI-generated shortcuts hide real liabilities, how to price a hardening phase, and how to turn findings into terms a board will trust. They also give you an independent, credible verdict you can’t sign off on alone.
What the findings mean
Clean results support the valuation. Gaps don’t necessarily kill a deal, but they become negotiating points: budget for a hardening phase, a maintainability discount, or a retention plan for whoever understands the code. The worst outcome is discovering after the deal that no one can safely change the product.
Key takeaways
- Run standard due diligence (code, security, scale, tests), then add AI-specific checks.
- The key AI risks are codebase comprehension, IP ownership, and platform lock-in.
- Ownership of AI-generated code is unsettled; confirm commercial rights in writing.
- Gaps are negotiating levers, not automatic dealbreakers; unmaintainable code is the real danger.
- Independent expert review turns a checklist into a verdict you can put in front of a board.
Lokesh Dudhat is the Co-Founder and CTO of SolGuruz, with 15+ years of hands-on experience in full-stack and product engineering. He spent over a decade building native applications across iPhone, iPad, Apple Watch, and Apple TV ecosystems before expanding into backend systems, Angular, Node.js, Python, AI software and solutions, and cloud architecture. As CTO, Lokesh defines and enforces engineering standards, architecture practices, and DevOps maturity across all delivery teams. He is actively involved in system design reviews, scalability planning, code quality frameworks, and platform architecture decisions for complex products. He works closely with product teams and enterprise clients to design resilient, maintainable, and performance-driven systems. His writing focuses on software architecture, headless CMS systems, backend engineering, scalability patterns, and engineering best practices.
Related questions
- Should I Build or Buy Enterprise Software for a Regulated Business?In a regulated business, build vs buy comes down to who carries the compliance risk. Buy when a certified product fits and the vendor signs the needed agreements (like a HIPAA BAA). Build when compliance, data control, or your core workflow is too critical to outsource. Many go hybrid: buy infrastructure, build the sensitive core.
- Should I Modernize My Legacy System or Rebuild It?Modernize first in most cases; rebuild only when the system truly can't be saved. If the core still works but is slow, dated, or hard to maintain, upgrading it in small, safe releases is faster, cheaper, and lower-risk. Rebuild when the technology is obsolete or unmaintainable. Start with an assessment to decide on evidence.
- How do you keep AI-generated code secure, maintainable, and production-grade?With the same controls you would apply to any production code, plus a few that are AI-specific. Every change goes through human review, automated tests, and security and dependency scanning before merge, and AI-suggested code is never shipped unread. For code that was generated quickly, or by a no-code tool, we run a focused audit covering security, architecture, and maintainability before it scales.
Your situation is different?
Evaluating an AI-built product before you invest or acquire?
Talk to Lokesh and team for an independent technical assessment you can put in front of a board.