Technical due diligence is the point in a funding round where someone who can read your code finally reads it. If you built your product with Cursor, Bolt, Lovable or Replit Agent, that moment can feel like an exam you never studied for. It doesn’t have to. We help founders through it regularly, and the pattern is consistent: the ones who prepare honestly usually close, and the ones who bluff usually don’t.
This article covers what investors and acquirers actually look at, why “built with AI” is rarely the problem, which findings sink or reprice deals, and how to get ready in the weeks before your data room opens.
What technical due diligence actually looks at
Most seed and Series A investors do not send in a forensic team. Often it is one senior engineer, sometimes a fractional CTO, working for a few days. They want to answer a small number of questions, and they answer them quickly.
First, does the product do what the deck says? They will click through it, then look at the repository to see whether the impressive parts are real or mocked. A dashboard that shows hard-coded numbers is a bad afternoon for everyone.
Second, can this codebase be worked on by a team? They check structure, naming, whether there is any documentation, and whether a new developer could run the app locally in an hour. A single 4,000-line file that does everything is a signal, and not a good one.
Third, what could go wrong in production? This is the security, reliability and cost review. They look for secrets committed to the repository, missing access controls, unbacked-up databases, and no way to tell when something has broken.
Fourth, who owns it? Licences of dependencies, ownership of the code, and whether any contractor or tool has a claim on it. Investors call this the IP check, and it is dull until it isn’t.
An investor code review is less about elegance than about risk. They are pricing the cost of making this thing safe to grow. If you want a grounding in what “quality” means before you sit down with them, our non-technical founder’s guide to code quality is a decent primer.
Why “built with AI” isn’t a dealbreaker, but hiding it is
A few years ago, an investor might have winced at a founder who couldn’t code. Now a large share of early-stage products have AI-written code in them, and investors know it. Nobody serious expects a hand-typed codebase from a two-person team. What they expect is that you understand what you have.
The trouble starts when the founder is vague. “Our engineers built it” when there are no engineers. “It’s fully tested” when there are no tests. Diligence reviewers are good at spotting these gaps because the repository tells the truth even when the pitch doesn’t. Commit history, generated file headers, tool config folders and leftover prompt comments all give the game away within minutes.
Once a reviewer catches one thing that was misrepresented, everything else you said gets re-examined with suspicion. We have seen a round stall for three weeks over a single overstated sentence, not because the code was terrible, but because trust had gone.
So say it plainly. “We built the first version with AI tools, and here is what we have done to make it safe.” That sentence costs you nothing and buys a lot. It turns the conversation from “what are you hiding” into “what is your plan”, which is a conversation founders can win.
The findings that kill or reprice deals
Most problems get fixed on a call and forgotten. A few change the price or end the process. Here is what we see most often.
Security holes
Exposed API keys, database tables readable by anyone, admin routes with no authentication, and user data flowing to places it shouldn’t. These are the fastest way to lose a deal, because they turn a technical concern into a legal and reputational one. A reviewer who finds a live payment key in your repository will stop the review and ask for a remediation plan before going further. If you haven’t checked yet, start with our security guide for AI-built apps. It covers the checks a reviewer will run first.
No tests, no safety net
An untested codebase is not a scandal on its own. Most early products have thin coverage. The problem is what it implies: every change is a gamble, so the investor’s new engineers will move slowly and break things. Reviewers often reprice for this, treating it as a few months of engineering time they will have to fund.
Single points of failure
One person holds the only login to the hosting account. The database lives on a free tier with no backups. A single cloud function handles every payment. The whole product depends on one AI-generated file nobody dares touch. Investors ask a blunt question here: what happens if this one thing fails, or if you get hit by a bus? “We don’t know” is a poor answer.
Licence and ownership issues
AI tools sometimes reproduce code from open-source projects, and dependencies bring their own licences. Most are fine. A few, such as strong copyleft licences in a closed-source product, can create real obligations. Reviewers also ask whether any freelancer or agency contributed without a signed IP assignment. This matters far more in an acquisition than in a seed round, but it is much easier to sort out now than during exit negotiations.
A preparation checklist
Give yourself four to six weeks if you can. Two weeks will do in a pinch. Work through this in roughly this order.
Start with access. Make sure you, not a contractor or a former co-founder, own the domain, hosting, repository, database and payment accounts. Use a password manager and turn on two-factor authentication everywhere. It sounds trivial, and it is the finding we see most.
Then scan for secrets. Search the repository and its history for keys, tokens and passwords. If you find any, rotate them, do not just delete them. Deleting a key from the latest commit does nothing about the copies in history. Our guide to finding exposed API keys walks through it step by step.
Next, check that your database access rules are actually on, and that you have backups you have tested restoring. An untested backup is a hope, not a backup.
Write a one-page architecture note: what the main pieces are, where they run, what each costs per month, and what talks to what. Reviewers love this because it shows you understand your own product. It also makes a later handover to a development team far smoother.
List your dependencies and their licences. Run the app on a clean machine from the README and fix whatever breaks. Add a handful of tests around the flows that make money: sign-up, payment, and whatever your core feature is. Ten good tests beat a hundred generated ones.
Finally, write down your known issues yourself. A founder who hands over a list of ten problems with a plan for each looks far more credible than one who claims to have none.
Fixing the worst issues before the data room opens
You will not fix everything, and you shouldn’t try. Triage by what would scare an investor most, in this order: anything that exposes user data or money, anything that would take the product down with no way back, and anything that blocks a new developer from starting work.
Security and access problems come first, because they are usually cheap to fix and expensive to leave. Backups and monitoring come second. Tests and cleanup come last, and you can present those as part of the funded roadmap, since investors expect to pay for some of it.
Then comes the harder question: is the codebase worth keeping at all? Occasionally a reviewer will suggest a rewrite, and you need to have thought about this before they do. Sometimes they are right. More often the code is rougher than it is broken, and a focused clean-up beats a rebuild by a wide margin. Our piece on when to rebuild and when to rescue lays out how we make that call, so you can walk in with an informed position rather than a defensive one.
One last opinion. The best time to find these problems is before anyone else does. An investor who discovers a flaw reprices the deal. A founder who discovers it and has already fixed it gets to tell a good story about how they operate.
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