Five things investors will find in your code before your series-A

The term sheet is signed. The technical due diligence kicks off. Then the investor’s technical advisor asks for read access to the repository, and the close slips by six weeks.

The product works. Customers are paying. But the advisor finds that one engineer holds the entire deployment process in his head, that production credentials sit in the Git history, and that nobody can say which parts of the codebase were written by a person and which by an AI agent. None of this kills the deal. All of it costs the founders leverage, time, and a line in the closing documents committing them to a remediation plan.

This did not happen to us. But every detail I described above comes from a codebase we have actually opened from one of our clients. I have yet to meet a founder who could not find their own company somewhere in that paragraph.

I run an AI-native software development company in Kitchener. A good share of our work starts the same way: a founder hands us a codebase and asks us to take it over, extend it, or get it ready for the next stage. Before we write a line, we read. Over roughly fifty of these read-only reviews, the same handful of problems shows up again and again. They are rarely about the quality of the code itself. They are about the things around the code that an outside technical reviewer looks for first.

Here are the five we see most often, why they matter at Series A, and how to fix each one before someone else finds it.

1. One engineer holds the whole system in their head

This is the most common finding and the most dangerous one. Early teams are small, so one person ends up owning the infrastructure, the deployment, the database migrations, and the one integration that nobody else has touched. It works until that person takes a vacation, gets a better offer, or burns out.

An investor sees this as key-person risk, and they price it in. More practically, it means your engineering velocity is capped at one person’s calendar, and your incident response is one phone number.

How to check: ask yourself who could deploy to production tomorrow if your lead engineer were unreachable. If the answer is nobody, or “me, but I would need to call them,” you have the problem.

How to fix it cheaply: have a second person deploy the next release, with the first person watching but not touching the keyboard. Write down every step they had to ask about. That document is the start of your deployment playbook, and the second deploy takes a fraction of the time the first one did while reducing the key-person risk.

2. No real line between staging and production

Many MVPs are built with one environment, and it is production. Features are tested on live customers. Database changes are run by hand against the real data. The team knows this is not ideal and plans to fix it “after the next release.”

A technical reviewer reads this as a sign that every future change carries customer-facing risk, and that the team has no safe place to reproduce a bug. It also tends to come with a second finding: no backups that anyone has ever restored from.

How to check: can you break something in a test environment today without a customer noticing? Have you ever restored your production database from a backup, end to end, and confirmed the data was intact?

How to fix it cheaply: a staging environment does not need to mirror production’s scale. It needs to run the same code, the same migrations, and a sanitized, sometimes anonymized, copy of the data. Most cloud providers make this a few hours of work. Then do one full backup restore and time it. Investors want to know your RTO and RPO. Knowing these numbers is worth more than the backup itself.

3. Secrets in the repository

API keys, database passwords, and cloud credentials committed to Git. Sometimes in a config file, sometimes in a .env that was added “just once” in the first week. Removing the file later does not help: Git remembers, and anyone with repository access can read the history.

This is the finding that turns a diligence conversation from “please fix” to “please fix before we wire the money.” For a health-tech or fintech startup, it can also be a reportable security event waiting to happen. It is also the cheapest of the five to prevent and one of the more tedious to clean up.

How to check: search your Git history, not just the current files, for strings like password, secret, api_key, and the prefixes your providers use (sk_live_, AKIA). Free tools such as gitleaks or trufflehog do this in minutes.

How to fix it cheaply: rotate every credential you find. Assume it is compromised, because you cannot prove otherwise. Move secrets into your cloud provider’s secret manager or at minimum into environment variables that never touch the repository. Add a pre-commit hook so it does not happen again. Rewriting Git history to remove the old values is optional once the keys are dead, and rarely worth the disruption.

4. AI-generated code nobody reviewed (Vibe-Coding?!)

This one is new, and it is growing fast. Founders are shipping more code than ever with AI agents, which is a good thing. The problem is not that the code was generated with AI. It is that nobody can say which parts were, whether a person read them, or whether they are tested.

We use AI in every step of our own development process, including the review itself: an AI pass reads every change and catches the obvious defects before a person ever looks. So this is not an argument against the tools. It is an argument for the same discipline you would apply to a new junior developer: everything they write gets read by someone senior before it ships. The AI review agent does not know whether a change fits your architecture, duplicates something that already exists, or quietly widens a security boundary. A senior engineer does. Technical reviewers are starting to ask about this directly. “Walk me through your AI usage and your review process” is now a standard diligence question, and “we don’t really have one” is a bad answer.

How to check: pick three recent pull requests at random. Was each one reviewed by a person other than the author? Do tests exist for the behaviour they changed? Could the author explain, line by line, what the code does?

How to fix it cheaply: make human review mandatory on every change to the main branch, even on a two-person team. Add a short section to your engineering notes describing which AI tools you use and what your review rule is. Investors do not expect you to avoid AI. They expect you to be in control of it.

5. No memory bank: nothing written down about how the system is built

Not a 40-page architecture document. A short set of living files in the repository that say what the main components are, where they run, how data flows between them, which decisions were made and why, and what the three biggest known risks are. We call this a memory bank. Most early-stage codebases we open have nothing of the kind; the information lives in the founders’ heads and in a Slack thread from eight months ago.

The cost shows up in three places. New hires take weeks longer to become productive. Every outside reviewer, whether an investor’s advisor during a tech due diligence, a security auditor, or a development partner like us, has to reconstruct the picture from scratch and bills you for the time. And when the person who knows leaves, the knowledge leaves with them, which brings us back to finding number one.

There is a fourth cost that is new: your AI tools are flying blind too. Claude Code/Codex that does not know your architecture, conventions, and past decisions will confidently generate code that contradicts all three. The same memory bank that onboards a human in a day is what makes AI-generated code fit your system instead of fighting it.

How to check: could a competent engineer who has never seen your system draw its architecture on a whiteboard after reading your repository for thirty minutes? Could your AI agent answer the same question?

How to fix it cheaply: you do not have to write it by hand. Point an AI agent at your repository and have it draft the memory bank: components, hosting, data flow, third-party dependencies, conventions, known risks. Then read it with your technical lead, correct what it got wrong, and add the decisions only a human knows. Push it to Git next to the code, so it is versioned and nobody has to ask where it is. Make updating it part of every significant change, ideally the pipeline; with AI writing the first draft, that is seconds, not hours.

A 30-minute self-check for this week

You do not need an outside reviewer to find most of this. Block half an hour with your technical lead and answer these honestly:

1. If our lead engineer were unreachable for two weeks, who could deploy, and how long would it take them to figure it out?
2. When did we last restore a production backup, and did we verify the data?
3. Have we ever scanned our full Git history for credentials?
4. Of our last ten merged changes, how many were reviewed by someone other than the author?
5. Is there a memory bank in the repository that a new hire, or an AI agent, could read to understand how the system fits together?

Two or more uncomfortable answers is normal for a pre-seed or seed company. It means you have a list, and a list is something you can work through before anyone else writes it for you.

What investor-ready actually means

None of this is a call to over-engineer. An MVP with a microservices architecture, a Kubernetes cluster, and 95 percent test coverage is usually a sign that the team built infrastructure instead of talking to customers. Technical reviewers know this, and they discount it.

What they are looking for is narrower: that the team understands its own system, that the obvious risks are known and either mitigated or consciously accepted, and that the next five engineers could join without a hero on the team. Those are organisational habits more than technical ones, and they cost days, not months.

The cheapest time to fix all five is before the term sheet, when nobody is watching and nothing is at stake. The most expensive time is after, when every week of delay has a dollar figure attached. If you are not sure where you stand, ask someone outside your team to read your code before an investor’s advisor does. A fresh pair of eyes finds in an afternoon what a team stops seeing after a year.

Benjamin Heinke is the founder and CEO of Cloudnonic, a Kitchener-based AI-native software development company and a member of Communitech’s Pro Squad. Cloudnonic builds and takes over software platforms, and takes B2B startups from idea to MVP to Series A. Founders in the Communitech community can book a free build review at cloudnonic.com.