Is Your AI-Built Codebase Investor-Ready? A Technical Due Diligence Checklist

Here is the reality nobody in the pitch room wants to say out loud: most startup codebases are now AI-generated or AI-assisted. Copilot, Cursor, Claude Code, ChatGPT — founders are shipping products faster than ever. And investors know it.

That is not inherently a problem. Speed matters. Getting to market matters. But AI-generated code carries risks that traditional technical due diligence was never designed to catch. And investors are catching on.

I have seen three deals stall in the last quarter alone because the technical DD uncovered issues that the founders did not even know existed. Phantom imports. Hallucinated API calls. Entire modules that referenced libraries that do not exist in any package registry on earth.

These were not bad founders. They were smart people who moved fast, used AI tools effectively, and built products that customers loved. But when the investor's technical advisor opened the codebase, what they found told a different story than the pitch deck.

If you are a founder about to raise, this post is for you. Run this checklist before the investor's technical advisor does.

What Investors Are Now Asking

The questions have changed. Five years ago, technical DD meant "does the product work?" and "is the architecture reasonable?" Today, smart investors — especially those who have been burned by AI-built startups that looked great on the surface — are asking questions that would have sounded absurd in 2023:

This is not a trick question. They are not trying to penalise you for using AI. They want to understand your team's actual capability versus the tool's output. If the answer is "most of it" and your team cannot explain how the core modules work, that is a risk factor. It means the team is dependent on a tool, not on understanding. And tools change. Understanding does not.

AI coding tools invent packages that do not exist. They import modules with confident, plausible names — utils.quantum_helper, @company/auth-middleware, flask.ext.sqlalchemy — that were never published to any registry. These are phantom imports, and they are everywhere in AI-generated codebases.

Your code might be importing something that literally does not exist, and it will not fail until that specific code path runs in production. The import sits there, dormant, until a user hits the right edge case. Then it crashes with a module-not-found error that nobody on your team has ever seen before, because nobody wrote that line — the AI did.

If one person holds all the context and the codebase has no documentation, no inline explanations, and no tests, the investor is looking at a bus factor of one. That is not a startup. That is a liability.

The investor is not just buying your product — they are buying the team's ability to maintain and extend it. If that ability walks out the door with one person, the investment is at risk from day one.

Not "did your CTO look at it." Not "we ran ESLint." An independent, external audit by someone who has no stake in the outcome. If the answer is "never," the investor has to assume the worst. And in my experience, they will. Because the founders who have nothing to hide are the ones who get audited voluntarily.

The Due Diligence Checklist

Here are the ten things you need to verify before you walk into that pitch meeting. These are not aspirational goals. These are not "nice to haves." These are table stakes for any startup that wants to be taken seriously by investors who do proper technical DD.

1. Health Score above 70 (C+ or above)
   A health score is a single number that captures the overall state of your codebase — imports, dependencies, documentation, test coverage, code smells, and more. Below 70 means there are structural issues that will cost real time and money to fix. Investors see a low health score the same way they see a low NPS: something is fundamentally wrong and the team either does not know or does not care. Neither interpretation helps your valuation.
2. Zero phantom imports or hallucinated APIs
   This is the big one. AI tools generate import statements for packages that do not exist. They call API methods that were never part of any library. They reference functions that sound right but were never defined anywhere in your codebase or in any dependency. A single phantom import means your code has a ticking time bomb — it works until it does not, and when it fails, nobody will understand why because the error will point to a module that was never real. Run a scan. Find them. Remove them. There is no acceptable number above zero.
3. No hardcoded localhost URLs or demo values in production
   You would be surprised how often AI-generated code includes http://localhost:3000 or api_key: "sk-demo-12345" or database_url: "postgres://user:password@localhost/mydb" in files that ship to production. These are not just sloppy — they are signals that nobody reviewed the code after the AI wrote it. An investor's technical advisor will find these in minutes. And when they do, the question becomes: what else was not reviewed?
4. Deprecated dependency count below 5% of total
   AI tools are trained on data that includes outdated packages. They will happily install a dependency that was deprecated two years ago because that is what appeared in their training data. A few deprecated packages are normal in any project — the ecosystem moves fast. But if more than 5% of your dependency tree is deprecated, it tells the investor your codebase is built on a foundation that is actively crumbling. It also means you are exposed to unpatched security vulnerabilities in packages that nobody is maintaining anymore.
5. Test coverage exists and tests actually assert something
   AI is very good at generating tests that look comprehensive but assert nothing meaningful. Tests that check expect(true).toBe(true). Tests that mock every dependency so thoroughly that they are testing the mocks, not the code. Tests that pass regardless of what the function actually returns. Having tests is not enough. The tests need to actually verify behaviour. If your test suite passes no matter what you change in the source code, you do not have tests. You have decoration. And a technical advisor will spot the difference in about thirty seconds.
6. Mother Code DNA coverage above 50%
   Mother Code DNA measures how much of your codebase explains itself — inline comments that describe why not just what, JSDoc annotations, README files, architectural decision records. If more than half your code has zero explanation of what it does or why it exists, the codebase is a black box. That means onboarding a new developer takes weeks instead of days. It means debugging takes hours instead of minutes. And the investor is paying for all of that wasted time through burn rate.
7. No circular dependencies in core modules
   Circular dependencies are a structural problem that makes code impossible to refactor, difficult to test, and fragile to change. Module A imports Module B which imports Module A — now you cannot change either one without risking breakage in the other. AI tools create circular dependencies constantly because they do not reason about module architecture. They generate code that satisfies the immediate prompt without considering the dependency graph. One circular dependency in a utility module is annoying. Circular dependencies in your core business logic are a red flag that the architecture was never designed — it was accumulated.
8. Deployment is documented and repeatable
   Can someone other than your lead developer deploy the application? Is there a script, a CI/CD pipeline, a documented process that someone can follow without calling anyone? If deployment lives in one person's head — a series of manual steps, environment variables set by hand, servers configured through SSH — the investor is looking at operational risk. "It works on my machine" is not a deployment strategy. It is a single point of failure dressed up as a process.
9. Bus factor greater than 1
   How many people need to be hit by a bus before the project stalls? If the answer is one, you have a single point of failure that no amount of revenue growth can offset. Investors are buying the team and the asset, not just the product. If the asset — the codebase, the deployment knowledge, the architectural understanding — is locked inside one person's brain, it is not really an asset. It is a dependency. And dependencies are risks.
10. No committed secrets or hardcoded API keys
    This should be obvious, but AI tools will happily generate code with placeholder API keys that look real, and developers will commit them without thinking. Once a secret is in your git history, it is there forever — even if you delete the file, even if you force-push, the commit is recoverable. A single committed AWS key, Stripe secret, or database password in your repo history is a security incident waiting to happen. It tells the investor your team does not have basic security hygiene, and it raises the question: if they committed secrets, what else did they miss?

Red Flags That Kill Deals

The checklist above is what "good" looks like. Here is what "deal is dead" looks like. These are the findings that make a technical advisor write "high risk — do not proceed" in their report. If any of these are true about your codebase, fix them before you pitch. Not after. Before.

How to Prepare

The good news: all of this is fixable. And you do not need to fix everything — you need to know where you stand and have a credible plan for the critical issues. Investors do not expect perfection from an early-stage startup. They expect awareness and a roadmap.

Step 1: See where you stand

Run a scan. Two seconds. No install required. No cloud upload. Your code stays on your machine, and you get an immediate, honest assessment of where things stand.

npx thuban scan .

You will get a health score, a letter grade, and a prioritised list of every issue in your codebase — phantom imports, deprecated dependencies, hardcoded values, missing documentation, circular dependencies, the lot. It is not a subjective opinion. It is a fact-based assessment against your actual files, your actual dependency tree, and your actual code structure.

The output will tell you exactly what an investor's technical advisor would find. Better to see it yourself first.

Step 2: Fix P1 and P2 issues before the pitch

You do not need a perfect score. Nobody has a perfect score. You need to show that you know what the issues are and that you have addressed the critical ones.

A health score of 75 with a clear roadmap for reaching 85 is far more impressive than a score of 60 with no awareness that there is a problem. The score itself matters less than the trajectory and the plan.

Focus on the items that kill deals: phantom imports, committed secrets, zero tests, hardcoded credentials. These are the things that make a technical advisor write "high risk" in their report. Everything else — documentation gaps, minor deprecations, code style inconsistencies — can be part of your improvement roadmap. Those are normal. Those are expected. The deal-killers are not.

The fact that you ran the scan, identified the issues, and fixed the critical ones before the pitch? That is a signal. It tells the investor you take your technical foundation seriously. Most founders do not do this. You should.

Step 3: Get a professional audit

A self-scan is a starting point. A professional Thuban Codebase Audit gives you a report you can put in your data room — an independent, third-party assessment of your codebase health that the investor's team can review without needing to clone your repo or run their own tools.

It covers everything on this checklist and more: architecture analysis, dependency risk assessment, security surface review, and specific remediation guidance with estimated effort for each issue. It is the technical equivalent of having audited financials — an independent verification that what you are selling is what you say it is.

Think of it this way: you would never walk into a Series A pitch without audited financials. Your codebase deserves the same treatment. It is the foundation everything else is built on.

Step 4: Include the health score in your data room

Put it right next to your financials, your cap table, and your customer metrics. A health score in the data room says: "We take our technical foundation seriously. We measured it. Here are the results. Here is our plan to improve."

That is a signal of maturity that most early-stage startups cannot match. While other founders are hoping the investor does not look too closely at the code, you are handing them the report proactively. That changes the dynamic of the entire conversation. You are not hiding from technical DD. You are leading with it.

The Cost of Not Doing This

Let me be direct about what happens when you skip this step.

The investor's technical advisor runs their own assessment. They clone your repo. They scan it. They read the dependency tree. They check the git history. They find the phantom imports you did not know about. They find the hardcoded localhost URLs. They find the deprecated dependencies, the missing tests, the committed API keys buried in a commit from six months ago that you forgot about.

Now you are on the back foot. Instead of presenting a clean codebase with a clear improvement roadmap, you are explaining why you did not know about critical issues in your own product. The conversation shifts from "here is our vision and our traction" to "here is our technical debt and why we did not catch it."

That is not a conversation you want to have in a pitch meeting. It is not a conversation you can win.

Best case: the valuation drops. The investor prices in the cost of fixing everything they found — the engineering hours, the refactoring sprints, the security remediation — and your pre-money comes down accordingly. I have seen 15-20% valuation haircuts from technical DD findings alone. On a $5M raise, that is $750K to $1M in additional dilution that was entirely avoidable.

Worst case: the deal collapses. The investor decides the technical risk is too high, or that the team's lack of awareness signals deeper problems with execution capability. The term sheet disappears. And now you have to explain to the next investor why the last deal fell through — which is a conversation that starts from a position of weakness no matter how you frame it.

Neither of these outcomes is necessary. The issues are findable. They are fixable. Most of them take hours, not weeks. You just have to look before someone else does.

Craig is the founder of Thuban and has reviewed codebases for technical due diligence across early-stage and growth-stage startups. If your code is going into a data room, it should be audited first.