The Technical Due Diligence Checklist Investors Actually Use (2026)

What is on an investor-grade technical due diligence checklist?

Seven areas: intellectual property and licensing, architecture and scalability against the plan, security and data handling, engineering process and delivery, team and key-person risk, cost structure and unit economics, and third-party dependency risk. Code quality sits inside the fourth and is rarely decisive on its own.

The framing that matters is that diligence is not an audit of engineering craft. It is an attempt to answer one question: does the technology plausibly support the revenue plan in the deck, and what would have to be true for it not to. Everything in the checklist is a way of asking that question from a different angle.

I have spent three-plus years advising institutional investors through Tegus, AlphaSights, GLG and Guidepoint, sitting on the analyst's side of exactly these calls, asked to assess whether a company's claims about its technology hold up. What follows is the structure those conversations actually take, not a template.

The most useful thing a founder can know: the questions are predictable, and preparing for them is a two-week exercise that meaningfully changes the outcome. If you want that run properly ahead of a raise, that is what technical due diligence engagements are for.

What are the seven areas every technical diligence covers?

IP and licensing first, because it is binary. Either the company owns its code or it does not. The failure mode is contractors and early collaborators without signed assignment, and it is astonishingly common in companies that started as a side project among friends. The second failure mode is a copyleft-licensed dependency sitting inside a distributed product, which can create obligations that materially change what an acquirer is buying.

Architecture and scalability second, always assessed against the plan rather than in the abstract. Nobody expects a seed-stage system to handle enterprise load. They expect you to know what breaks first, at roughly what volume, and what the fix costs. A founder who says "the write path on the primary database at about fifty times current volume, and the fix is read replicas plus a queue, roughly three engineer-weeks" is more reassuring than one who says the system scales.

Security and data handling third. Where does customer data live, who can reach it, how is access logged, and what happens if a laptop is stolen. For anything touching regulated data, add the framework question: which controls exist today, which are planned, and what has been externally validated. Common frameworks referenced in these conversations are SOC 2's Trust Services Criteria, ISO 27001, and for secure development practice, NIST's Secure Software Development Framework.

The remaining four (engineering process, key-person risk, cost structure and third-party dependency) are where the assessment moves from a checklist to a judgement. They are also where a prepared founder can look dramatically better than an unprepared one for very little work.

The checklist itself

Below is the version I work through. It is deliberately answerable: every item is something a founder can produce or state in a sentence, and an item you cannot answer is itself a finding. Work through it before an investor does, and treat every unchecked line as a decision about whether to fix it or disclose it.

The single highest-value preparation is the disclosure list. A known problem raised by the founder is a negotiating item; the same problem discovered by the analyst is a trust event. I have watched both happen to comparable companies with very different outcomes, and the difference was entirely in who said it first.

What actually kills deals?

Ownership gaps, not code quality. The pattern is nearly always the same: an early contributor was never put under a proper agreement, or a contractor's contract assigned nothing, and the company therefore cannot demonstrate clean title to part of its own product. This is fixable retroactively only with the cooperation of a person who now has leverage, which is why it is so expensive.

Licence contamination is the second. A copyleft-licensed component inside a product you distribute can create obligations that a strategic acquirer's legal team will not accept, and the discovery usually happens late because nobody generated the inventory. Generating that inventory costs an afternoon with standard tooling.

Key-person risk is third and the most under-managed. If one person is the only human who understands the billing engine, that is a finding, and pretending otherwise fails immediately under questioning. The correct response is not to claim redundancy you do not have; it is to name the risk and show the mitigation plan with dates. Diligence rewards accurate self-assessment far more than it rewards a clean story.

Security posture is fourth and is priced rather than fatal, except where the acquirer's own customers impose requirements. If your buyer sells to healthcare or financial services, their compliance obligations become your engineering backlog on day one after close, and that cost lands in the valuation.

What almost never kills a deal on its own: test coverage, code style, a monolith, or an unfashionable framework. Investors are diligencing a business. A working monolith with clear ownership beats an elegant microservice architecture that one person understands.

“A known problem raised by the founder is a negotiating item. The same problem found by the analyst is a trust event. Everything else in diligence preparation is downstream of that one sentence.”— The most useful thing I tell founders before a raise

How do you diligence a codebase that was substantially written by AI?

This is the genuinely new question in 2026 and most templates have not caught up. Stack Overflow's 2025 survey found 84% of developers using or planning to use AI tools, so the honest prior for any company founded in the last two years is that a meaningful share of the codebase was model-generated. The question is not whether, it is what that changes.

Three things change. Provenance: can the company state which parts were generated and under what review process. Licence exposure: generated code can reproduce training data, and the company should be able to say what scanning it runs. Review depth: generated code that passed a skim is the specific risk, because plausible-but-wrong output survives a skim by construction, the same mechanism that made METR's experienced developers 19% slower while feeling faster.

What I look for is not a policy document. It is evidence of a review gate that assumes plausibility rather than correctness, applied to the parts where being wrong is expensive: migrations, permission policies, money handling and idempotency. A company that can say "generated code is fine anywhere except these four categories, and here is the pull-request template that enforces it" has thought about the problem. A company that says its engineers review everything carefully has not.

The second-order question is maintainability. Code nobody wrote is code nobody remembers, and a system where no engineer can explain why a module is shaped the way it is has a bus factor of zero rather than one. Ask the team to walk through an unfamiliar module live. The answer is informative within two minutes.

What should a founder do in the ninety days before a raise?

Treat diligence preparation as a project with a deadline, because the alternative is doing it under time pressure while also running a fundraise. The work is unglamorous, largely non-engineering, and reliably changes the tone of the conversation.

Front-load the legal work, because it is the only category with a hard dependency on other people. Chasing an IP assignment from a contractor who left two years ago takes as long as it takes, and it cannot be compressed by working harder in week eleven.

Then produce the artefacts: architecture diagram, data inventory, licence inventory, four delivery metrics, bus-factor map, margin analysis. None of these takes long individually. All of them together take about two weeks of a senior engineer's time, and having them ready shortens diligence by weeks because the analyst stops having to extract them.

Finally, write the disclosure list and price it. Every known problem, what it would cost to fix, and whether you plan to. This is the artefact that most changes the dynamic, and it is the one almost nobody produces.

What does technical due diligence cost and how long does it take?

For an early-stage company, a focused technical diligence is a one-to-three week engagement: code and architecture review, three to six interviews with engineers, an infrastructure and cost review, and a written report with findings ranked by severity and cost to remediate. Larger or regulated targets extend it, mostly because the security and compliance surface grows.

The output that matters is not the report length, it is whether findings are costed. "Test coverage is low" is not actionable. "Test coverage is low in the billing subsystem specifically, which is also the subsystem with a bus factor of one, and closing both is roughly four engineer-weeks" is a decision an investment committee can act on.

For founders, the same work run pre-raise is a rehearsal with a fix list attached. For investors, it is an independent read from someone who has been on both sides of the table: three-plus years advising institutional investors through Tegus, AlphaSights, GLG and Guidepoint, and a decade of building the kind of systems being assessed.

Related reading on the two areas that generate the most findings: startup engineering team structure for the key-person and process questions, and the SaaS MVP tech stack for 2026 for the architecture and cost-structure ones. The engagement itself is technical due diligence.