Technical due diligence is how investors confirm that the engineering behind a startup is real: that the product works, the codebase can scale, the security holds, and the company actually owns what it built. It happens before a term sheet, and increasingly before the first deep call. Here is what investors check, and how to arrive ready instead of scrambling.
In short, technical due diligence checks seven things: system architecture and scalability, code quality and test coverage, security posture, open source dependencies and their licenses, who owns the intellectual property in the code, key-person risk on the engineering team, and how much of the codebase was written by AI. The rest of this guide explains each one, stage by stage, and how to be ready for it. If you cannot read code yourself, your job is to make sure your technical cofounder or lead engineer can produce the artifacts below; this guide tells you which ones matter.
Most founder advice treats due diligence as a paperwork phase you survive after the handshake. Technical due diligence is the opposite. It is the moment an investor stops trusting your slides and starts reading your repository. A polished demo and a clean pitch deck describe what you want to be true. The code, the cloud bill, the dependency tree, and the commit history describe what is actually true. The gap between those two is exactly what a technical reviewer is paid to find.
This guide walks through what investors look at, why a working product is not the same as a sound one, and how to build the proof in advance so the same checks do not slow down every round. It sits alongside our and guides; together they cover the three lanes a serious investor runs in parallel.
What technical due diligence actually checks
Technical due diligence is a structured review of everything that makes the product run: the system architecture, the quality of the code, the security posture, how the product scales, the open source it depends on, who owns the intellectual property, and whether the engineering team can keep delivering. The reviewer is usually a fractional CTO, a specialist firm, or a technical partner at the fund. Their job is to turn your engineering claims into findings a non-technical partner can act on.
The reason this lane exists is that software risk is invisible from the outside. A product can look finished and still sit on a foundation that cannot survive its next ten thousand users. , which analyzed 211 million changed lines of code authored between January 2020 and December 2024, found that refactoring fell from 25% of changed lines in 2021 to under 10% in 2024, while copy and pasted code rose from 8.3% to 12.3% of changed lines. In 2024, for the first time, cloned code exceeded reorganized code. A demo cannot show you any of that. A code review can.
For a broader map of how technical, financial, and legal checks fit together at the earliest stage, our is the companion piece. This article goes deep on the technical lane specifically.
What a polished demo hides
A demo is a curated surface. You choose the happy path, the clean dataset, and the one flow that always works. Investors know this, because they have all been shown the glossy version of something fragile. So technical due diligence is built to look underneath the surface at the parts you did not stage.
Three things hide there. The first is security. , drawn from 12,195 confirmed breaches, found that exploitation of software vulnerabilities was the initial vector in 20% of breaches, up 34% year over year, while stolen login details drove another 22%. For an acquirer or a later-stage investor, that risk has a price tag: , based on 604 organizations, put the global average breach at $4.88 million, a 10% jump over the prior year.
The second is your dependency tree. Modern startups are assembled as much as they are written, and the open source underneath is where most of the latent risk lives. , based on 965 commercial codebases across 16 industries, found that 86% of codebases contained open source vulnerabilities, 81% contained high or critical risk vulnerabilities, and 90% had components more than four years out of date. It also found license conflicts in 56% of codebases, a direct threat to who owns what.
The third is maintainability, the slow tax that decides how fast you can ship next year. Stripe's study, a 2018 survey of more than 300 development teams, found developers spend over 17 hours a week on maintenance, with about a quarter of that time spent fixing bad code, a combined drag Stripe valued at roughly $85 billion a year. The number is old, but the mechanism is not. Bad code does not announce itself in a pitch. It shows up as the reason the roadmap keeps slipping.
What investors look at, line by line
A technical reviewer works through a consistent set of areas, and each one maps a claim you have made to a check that confirms whether it holds. The table below pairs the founder claim with what the reviewer examines and the proof that actually settles the question.
What you claim | What technical due diligence checks | The proof that settles it |
|---|---|---|
"Our architecture scales" | System design, data model, single points of failure, load behavior under growth | Architecture diagram, load test results, a documented path from current to 10x traffic |
"The code is solid" | Repository structure, test coverage, documentation, code churn, refactoring discipline | Test coverage numbers, CI logs, a codebase a new engineer can read in a day |
"It is secure" | Auth model, secrets handling, known vulnerabilities, patch cadence, data handling | A recent dependency scan, basic penetration test, encryption and access controls |
"We use modern open source" | License compatibility, component age, unmaintained packages | A software bill of materials with licenses cleared and stale packages flagged |
"We own our IP" | Whether code authors assigned rights, contractor agreements, copied code | Signed IP assignment from every contributor, clean provenance on key modules |
"The team can deliver" | Key-person risk, bus factor, on-call coverage, hiring plan | More than one person who understands each critical system, written runbooks |
"AI helped us move fast" | How much code is AI generated, whether it was reviewed, clone rate | Review trail on generated code, duplication kept in check, code the team can maintain |
Security sits at the center of all of it. As , put it, "As open source adoption continues to grow at an incredible velocity, businesses need to implement robust software composition analysis and risk management strategies to build trust into their applications, data and intellectual property." Trust is the operative word. The whole exercise is an investor deciding whether the technical story they were told can be trusted with their money.
Two rows in that table catch founders off guard more than the rest. Scalability is the first. Most early teams build for the traffic they have, then describe the system as if it were built for the traffic they want. A reviewer probes the gap by asking what breaks at 10x: the database, the queue, a third-party rate limit, or the one synchronous call buried in the signup flow. Key-person risk is the second. When a single engineer is the only person who can explain or safely change a core system, the company is one resignation away from a frozen roadmap. Neither of these shows up in a demo, and both are cheap to address before a reviewer raises them and expensive to explain away after.
The AI-generated code question nobody asked two years ago
One area is new enough that many founders have not thought to prepare for it: how much of your codebase was written by an AI assistant, and whether anyone reviewed it. This now belongs in technical due diligence because the data says unreviewed AI output degrades a codebase in measurable ways. GitClear's finding that code cloning grew fourfold and refactoring collapsed over the same period that AI assistants went mainstream is not a coincidence a reviewer ignores. AI can produce code that runs and still leave you with duplication, weak test coverage, and modules nobody on the team fully understands. A reviewer in 2026 will ask which parts of the system were generated, what review they passed, and whether your engineers can maintain code they did not write. Having a clear answer is itself a signal of engineering maturity.
How deep does technical due diligence go at each stage?
The depth scales with the round. A pre-seed angel is not going to commission a six-week security audit, and a Series A lead is not going to wave the codebase through on trust. The table below shows what the review actually covers at each stage, who runs it, and the single artifact that matters most when you get there.
Stage | What the review covers | Who runs it | The artifact that matters most |
|---|---|---|---|
Pre-seed | Does the product work, is the code yours, any glaring security holes | Often the angel or investor themselves, lightly | Signed IP assignment from every contributor |
Seed | The above, plus architecture, test coverage, and dependency and license health | A technical partner at the fund, or a fractional CTO (a part-time senior engineer brought in for the review) | A clean dependency and license scan |
Series A | A full review: scalability under load, a security audit, key-person risk, and code maintainability | A specialist technical due diligence firm | A documented path from current scale to 10x traffic |
The pattern is that proof scales with stage, so the artifacts you build early keep paying off. A clean IP assignment that satisfies a pre-seed angel is the same document a Series A firm checks two years later. Build each piece once, keep it current, and every later review starts from a stronger position. If you have no engineer in house yet, the pre-seed bar is still reachable: a working product, code you can show you own, and no obvious security holes.
Why every investor re-runs the same checks, and what that costs you
Here is the part that quietly drains early-stage founders. Technical due diligence does not transfer. Every fund that gets serious runs its own version, because conviction built inside one firm cannot be handed to another. The reviewer your lead investor hired produces a report your next investor will not see and would not trust if they did. So you answer the same architecture questions, grant the same repository access, and walk through the same security posture, round after round, and again if you are ever acquired.
That repetition is expensive in two ways. The obvious one is time: weeks of your best engineers pulled off the roadmap to host reviewers. The quieter one is the risk it creates for you. Thin, inconsistent diligence is dangerous for the founder, because a rushed reviewer fills the gaps with assumptions, and assumptions about code tend to be pessimistic. Research by , who measured the duration of pre-investment meetings using cell-phone signal data, found that less due diligence is associated with hotter deals, busier investors, and greater distance, and that thinner diligence goes hand in hand with more volatile investment performance. The reviewers checking your work are not all reading at the same depth. When they are rushed, the founder who can hand over clean, organized proof is the one who gets a fair read instead of a nervous pass.
Justin Leader, CEO of Human Renaissance, frames the stakes from the buyer's side in the firm's : an acquirer inheriting a portfolio company inherits its technical debt along with its revenue. Every investor who looks at you is doing a smaller version of that same calculation. The faster you can prove the debt is understood and contained, the less it weighs against you.
Building technical proof once, then keeping it live
The way out of re-diligencing yourself every round is to treat technical credibility as something you establish once and keep current, rather than reassemble from scratch each time an investor asks. This is the problem SeedForge was built to solve. A founder runs one 30-minute AI session and connects real signals from the business, and SeedForge produces a Living Profile: a structured, shareable view of the company that an investor can explore before the first call. Instead of a deck that asserts the architecture is sound, the profile carries the structured proof, so the investor arrives already knowing what is real and the conversation starts one level deeper.
The proof does not go stale. The profile stays live as your codebase, traction, and team evolve, which matters because technical reality changes faster than a fundraising deck can keep up. And because the profile is yours, it does double duty: SeedForge can run matched investor outreach on your behalf, from your own account, on a pay-per-outcome basis, while investor AI agents continuously watch profiles that fit their thesis. You build the proof once, and it keeps working in the background while you go back to building the product. That is the shift from fundraising as a months-long interruption to fundraising as something that runs continuously underneath the work. One link, at seedforge.com, carries it.
SeedForge does not stand in for an investor's own review. A good investor will still dig in, and should. The point is that when they do, the engineering story holds up because the proof was ready before the term sheet appeared. The same logic underpins how thoughtful angels approach the earliest checks, covered in our guide to .
A pre-diligence technical readiness checklist
You do not need an enterprise security program at pre-seed. You need to have honest answers ready and a few artifacts that show you take the engineering seriously. Four artifacts do most of the work: a one-page architecture document, your test coverage number, a software bill of materials, and signed IP assignment from every contributor. Work through the full list in order, because each step removes a question a reviewer would otherwise have to chase.
Write one architecture document. A single page with a system diagram, the main data flows, and where the bottlenecks are. If you cannot draw it, that is the first finding.
Know your test coverage number, and your CI story. You do not need 90% coverage. You need to know what your number is, what it covers, and that your pipeline runs.
Run a dependency and license scan. Produce a software bill of materials, a list of every open source component you use and its license. Flag anything more than four years out of date and clear every license conflict before an investor's tool finds them first.
Cover the security basics. Secrets out of the codebase, encryption in transit and at rest, sane access controls, and a recent scan. Map your gaps before someone else does.
Confirm you own your code. Every founder, employee, and contractor should have signed IP assignment. This overlaps with the , and it is where copied or AI-generated code can quietly create ownership questions.
Reduce key-person risk. If one engineer is the only person who understands a critical system, write the runbook and pair someone on it. A bus factor of one, where a single irreplaceable person holds all the knowledge, is a red flag in every review.
Document your AI-assisted code. Know roughly how much of the codebase was generated, show it was reviewed, and keep duplication in check.
A founder who can produce these makes the review easy, and in doing so demonstrates the exact engineering discipline the review is trying to confirm exists. The checklist and the proof are the same thing. The same readiness mindset carries over when investors move from the code to the numbers, which is where picks up the thread.
Technical due diligence rewards the founders who treated their engineering as something to be shown, not something to be hidden until forced. Build the proof early, keep it current, and the part of fundraising that scares first-time founders most becomes the part that sets you apart.
Frequently asked questions
What is technical due diligence for a startup?
Technical due diligence is a structured review investors run to confirm a startup's technology is sound. A technical reviewer examines the architecture, code quality, security posture, scalability, open source dependencies, and IP ownership, then reports whether the engineering claims in the pitch hold up before money changes hands.
When does technical due diligence happen in a funding round?
It usually begins after initial interest and before a term sheet is signed, running in parallel with financial and legal review. Increasingly, lighter technical checks happen even earlier, because investors want to confirm the product is real before committing time to a deep first call.
Do pre-seed and seed startups get technical due diligence?
Yes, though it scales with stage. At pre-seed, a reviewer may simply confirm the product works, the code is yours, and there are no glaring security holes. By Series A, expect a full review of architecture, test coverage, dependency licenses, and key-person risk by a specialist or fractional CTO.
What do investors check in a code review?
They look at repository structure, test coverage, documentation, code churn, and how much code is duplicated or unmaintained. Black Duck's 2025 analysis found 86% of commercial codebases contain open source vulnerabilities and 90% have components over four years out of date, so dependency health gets close attention.
Does AI-generated code hurt technical due diligence?
It can, if it goes unreviewed. GitClear's 2025 research found code cloning grew fourfold and refactoring fell below 10% of changes as AI assistants spread. Reviewers now ask how much of your code was AI generated and whether your team reviewed and can maintain it. A clear answer signals maturity.
How do I prepare for technical due diligence?
Write a one-page architecture document, know your test coverage number, run a dependency and license scan, cover security basics, confirm signed IP assignment from every contributor, and reduce key-person risk. Having these artifacts ready turns a slow, defensive review into a fast confirmation that your engineering holds up.