Code Audit
Know exactly what you're working with before you bet on it.
When You Need an Outside Opinion
You’re about to make a big decision. Raise a round, acquire a company, scale a product, or hand a codebase to a new team. The question nobody can answer confidently: is this code a foundation or a liability?
Your developers say it’s fine. The seller says it’s great. The founder says it works. And they might all be right. But “works” and “ready for what’s next” are different things. A prototype that handles 100 users can collapse at 10,000. An app that passes basic tests can have security holes that make investors nervous. Code that ships features today can be unmaintainable in six months.
You need someone who’ll look at the code without an agenda and tell you what’s actually there. Not what should be there. Not what someone planned to build. What exists right now, what it can handle, and what it can’t.
That’s what a code audit does. It gives you the information you need to make a confident decision — whether that decision is to invest, acquire, scale, or walk away.
Why Code Audits Matter Now
Forty-one percent of all code committed on GitHub is now AI-generated. A quarter of Y Combinator’s Winter 2025 batch was built with 95% or more AI-generated code. The barrier to shipping a working product has never been lower. That’s great for innovation. It’s terrible for quality assurance.
AI tools generate code that works in the demo. It passes the acceptance test. It ships the feature. But it also introduces patterns that experienced engineers would never write. Duplicated logic across files. Missing error handling for edge cases. Security shortcuts that aren’t visible in the UI. Hallucinated API calls that happen to work against the current version of a library but will break on the next update.
Investors are catching on. Technical due diligence used to be a formality — a quick check before closing. Now it’s a gate. Smart money wants to know whether the codebase they’re betting on is real engineering or a stack of AI-generated patches held together with optimism. If you’re raising, you need to know the answer before they ask the question.
And if you’re acquiring, the stakes are even higher. You’re buying the code. Every architectural decision, every security shortcut, every untested edge case becomes your problem on day one. A thorough audit before close saves you from discovering six-figure problems after the wire transfer.
The old assumption — that code written by professional developers follows professional patterns — no longer holds. You can’t tell by looking at the commit history. You can’t tell by running the app. You need someone to read the code, understand the architecture, and tell you what they find.
What We Audit
Architecture and code organization. Is there a coherent architecture, or did the codebase grow organically without a plan? Are concerns separated? Can a new developer understand the structure in a day? We evaluate the overall design decisions and whether they’ll support the next stage of growth.
Security posture. Authentication and authorization implementation. Data handling and encryption. Input validation and output encoding. Dependency vulnerabilities. Secrets management. OWASP top 10 coverage. We look for both the obvious holes and the subtle ones — the places where “it works” masks “it’s exploitable.”
Test coverage and quality. Not just coverage percentage — we evaluate whether the tests actually catch bugs. Are critical paths tested? Are edge cases covered? Do the tests test behavior or implementation details? Can you refactor with confidence, or does every change break twenty tests that shouldn’t care?
Performance and scalability. Database query patterns. N+1 queries hiding behind ORMs. Missing indexes. Memory leaks. Caching strategy (or lack thereof). Connection pooling. We identify the bottlenecks that don’t matter at current scale but will matter at 10x.
AI-generated code patterns. This is increasingly the biggest category. We identify code that was generated by AI tools — the patterns are distinctive — and evaluate whether it was reviewed, tested, and integrated properly. AI-generated code that was guided by an experienced developer is different from AI-generated code that was accepted without scrutiny.
Dependency health. Outdated packages. Known vulnerabilities. Abandoned libraries. License compliance issues. Dependencies that pull in half the internet. We evaluate the supply chain risk of your dependency tree.
Infrastructure and deployment. How the code gets from repository to production. CI/CD pipeline quality. Environment management. Secrets handling. Monitoring and observability. The gap between “works on my machine” and “runs reliably in production.”
How We Do It
Scope (1-2 days). We start with a kickoff conversation. What decisions does this audit inform? Are you raising? Acquiring? Scaling? Hiring? The answers determine where we focus. A pre-fundraise audit emphasizes different things than a pre-acquisition due diligence review. We agree on scope, timeline, and deliverables.
Analysis (3-5 days). We read the code. All of it. We run static analysis tools, but tools are the starting point, not the conclusion. We trace data flows. We evaluate architectural decisions in context — what made sense when the team was two people might not make sense now that you’re hiring ten. We identify patterns, anti-patterns, and the code that will cause problems six months from now.
Report (1-2 days). We compile everything into a structured report. Every finding is categorized by severity and effort. Critical security issues. High-impact architectural problems. Medium-priority improvements. Low-priority cleanup. Each finding includes what we found, why it matters, and what to do about it.
Walkthrough (half day). We walk you through the report live. We explain what we found, answer questions, and help you prioritize. If you’re using this for fundraising, we help you frame the findings for investors. If you’re using it for an acquisition, we give you the technical perspective you need for negotiation.
What You Get
Audit report. A comprehensive document covering every area we evaluated. Findings are specific — file names, line numbers, concrete examples. Not abstract advice. Actionable intelligence.
Architecture assessment. An honest evaluation of the system design. What’s solid. What’s fragile. What will need to change as you scale. Includes a visual architecture diagram that maps the actual system — not the idealized version.
Risk matrix. Every finding plotted on a severity-versus-effort matrix. Critical issues that are quick to fix go first. Low-severity issues that require major refactoring get deprioritized. You know exactly where to spend your engineering budget.
Remediation roadmap. A phased plan for addressing the findings. What to fix before you raise. What to fix before you scale. What can wait. Each phase has estimated effort so you can budget engineering time realistically. If the findings point to technical debt cleanup, we’ll scope that out specifically.
Executive summary. A non-technical overview for founders, investors, and board members. The state of the codebase in plain language. Risks, strengths, and recommendations without the jargon.
When This Makes Sense
Before raising a Series A or B. Investors will do their own technical due diligence. Better to know what they’ll find before they find it. Fix the critical issues, have honest answers for the rest, and walk into the process with confidence.
Evaluating an acquisition target. You’re buying the code, not just the product. An audit tells you what you’re actually getting — the real engineering quality, not the demo. It gives you leverage in negotiation and clarity on post-acquisition integration costs. PE firms use our audits to assess portfolio company acquisitions. Corporate M&A teams use them to quantify integration risk before the LOI.
Pre-investment technical diligence. VCs and growth equity firms increasingly require independent technical due diligence before writing checks. We provide the assessment your investment committee needs — architecture quality, scalability ceiling, security posture, and technical debt quantification. The report is designed for non-technical stakeholders who need to make investment decisions with confidence.
Inheriting a codebase. A key developer left. An outsourced team delivered something and disappeared. You acquired a product. Now you need to understand what you have before you build on it.
Scaling an AI-built MVP. You built fast with Cursor, Bolt.new, Lovable, or another AI tool. It worked. Now you need to know whether the foundation can support what’s next. This is increasingly the most common reason founders come to us.
Before hiring engineers. You’re about to invest in a team. Make sure you’re handing them a codebase they can be productive in — or know what needs to change first.
For Investors and Acquirers
If you’re evaluating a deal, your audit needs are different from a founder’s. You don’t need a development roadmap. You need a risk assessment that informs a transaction.
What’s different about an investor-facing audit. We deliver a deal-oriented report designed for investment committees and deal teams. The executive summary is written for non-technical decision-makers. Findings are quantified in terms of remediation cost and timeline — the numbers you need to adjust valuation, structure earnouts, or decide to walk away. We include integration cost estimates for post-acquisition engineering work.
PE and VC use cases. Growth equity firms use our audits during due diligence on software acquisitions in the $5M-$100M range. VCs use them pre-investment to validate that the technology behind a pitch deck is real. Portfolio companies use them to assess bolt-on acquisitions. Corporate development teams use them to evaluate build-vs-buy decisions.
How we handle deal-sensitive engagements. We work under NDA. We’re experienced with data room access, seller-mediated code access, and the time pressure of deal timelines. We can deliver a focused assessment in as little as one week when deal timing requires it. We’ve worked alongside legal, financial, and commercial diligence teams and know how to coordinate deliverables.
The AI-generated code question. This is increasingly the first question investors ask. With 41% of GitHub commits now AI-generated and a growing number of startups built primarily with vibe coding tools, the risk profile of software assets has changed. We evaluate whether AI-generated code was properly supervised, tested, and integrated — or whether it’s a liability masquerading as an asset.
What Comes Next
An audit gives you information. What you do with it depends on what you find.
If the codebase is solid, you’ve got a green light. Scale with confidence. Raise with data. Acquire knowing what you’re getting.
If it needs work, you have a roadmap. For AI-generated codebases that need restructuring, our vibe code cleanup service picks up where the audit leaves off. For broader technical debt, our technical debt cleanup process addresses the systemic issues.
Either way, you stop guessing and start deciding with evidence.
Ready for an Honest Assessment?
The code doesn’t lie. But it doesn’t volunteer information either. An audit surfaces what’s there — good and bad — so you can make your next move with confidence.
Get in touch to scope a code audit. We’ll tell you what we find, what it means, and what to do about it.