Technical due diligence for venture funding follows a predictable pattern. Engineering leaders prepare extensive documentation: test coverage percentages, sprint velocity charts, deployment frequency metrics. Then they wait for questions about architecture, code quality, and development practices. The questions, when they arrive, are different.
Investors want to know which customers trust the product with their operations. They ask about service-level agreements and whether they have been met. They inquire about team retention and how long it takes new engineers to contribute meaningfully. The technical metrics that engineering teams obsess over receive cursory attention. The business outcomes those metrics supposedly predict receive intense scrutiny.
The Hierarchy of Evidence
This pattern emerged across three funding rounds at a blockchain infrastructure company that ultimately reached unicorn valuation. In each round, due diligence began with the same materials: architecture diagrams, security audits, performance benchmarks, development processes. In each round, the conversation moved quickly past these documents to three questions.
First: can the technology deliver what the business promises? The evidence investors seek is not theoretical capacity but demonstrated performance. A startup claiming to serve enterprise clients must name those clients. A platform promising reliability must show uptime data from production systems under real load, preferably during periods of stress. Assertions about scalability matter less than evidence of having already scaled.
Second: can the team build what comes next? Here the relevant metrics are retention rates and time-to-productivity for new hires. A 96% retention rate, unusual in high-growth environments, signals that engineers choose to stay despite abundant alternatives. New engineers contributing meaningfully within weeks rather than months suggests systems are comprehensible and processes are effective. These outcomes are difficult to fake.
Third: what could break? Investors expect technical risks to exist. They become concerned when leadership cannot articulate them clearly or lacks credible mitigation plans. A system with no single points of failure is either trivial or its architects do not understand it. The question is whether risks are known, monitored, and managed.
Why Surface Metrics Mislead
The engineering metrics that teams typically emphasize—test coverage, story points, lines of code—share a common flaw: they measure activity rather than outcomes. High test coverage can coexist with unreliable systems if tests verify the wrong behaviors. Impressive deployment frequency means little if deployments regularly break production. Velocity is meaningless without context about what is being built and whether it works.
These metrics persist because they are easy to collect and compare. But their relationship to business success is indirect at best. A company can achieve exemplary scores on all conventional engineering metrics while building a product that fails to meet customer needs, cannot scale to meet demand, or requires constant manual intervention to remain operational.
Investors have learned to look past these indicators to evidence of actual capability. Revenue per engineer matters because it captures efficiency: how much value the team generates relative to its cost. Customer adoption by sophisticated buyers—enterprises, institutions, regulated entities—matters because these organizations conduct their own rigorous evaluations. A bank that trusts your infrastructure with its operations has performed due diligence more thorough than any venture firm can afford.
The Documentation That Actually Matters
Effective preparation for technical due diligence requires different materials than most teams assemble. Start with the architecture, but emphasize decisions rather than diagrams. Why was this database chosen? What tradeoffs were made? Where are the bottlenecks, and what happens when they are reached?
Document the team's structure and growth. Not an org chart, but retention data, hiring velocity, time-to-productivity metrics, and evidence of how technical leadership is distributed. Investors want to understand whether the organization can double in size without collapsing, and whether it depends dangerously on specific individuals.
Describe the delivery process with specifics about cycle time, deployment frequency, and incident response. But frame these in terms of customer impact. How quickly can critical bugs be fixed? How often do deployments cause outages? When systems fail, how long until recovery? These questions connect technical practices to business outcomes.
Finally, maintain a frank assessment of technical risks. Which components would cause maximum damage if they failed? Where is the technical debt most problematic? What dependencies create vulnerability? Investors expect problems; they become alarmed when management seems unaware of them.
The Signal in Customer Names
Perhaps the most revealing aspect of technical due diligence is which evidence investors find most persuasive. During one funding round, extensive documentation of testing practices, security protocols, and development methodology received polite acknowledgment. Then came the question: "Which banks are using this?"
The answer—Meta, JP Morgan, several others—effectively concluded the technical discussion. These organizations employ sophisticated evaluation processes and maintain exacting standards. Their willingness to build critical infrastructure on the platform signaled technical credibility more powerfully than any metric could. The implicit logic: if your technology works for them, it probably works.
This pattern reveals what technical due diligence fundamentally assesses. It is not a detailed audit of code quality or development practices, though these receive attention. It is an evaluation of whether the technology can deliver what the business model requires, whether the team can build what comes next, and whether leadership understands the risks involved. Evidence of meeting these requirements comes not from metrics but from demonstrated performance with demanding customers.
How to Prepare When It Matters
The companies that navigate technical due diligence most effectively share certain characteristics. They document their technology clearly but emphasize decisions and tradeoffs rather than attempting to present a flawless system. They acknowledge technical debt and risks while demonstrating plans to manage them. They show evidence of actual performance rather than theoretical capacity.
Most importantly, they understand that investors are evaluating the team as much as the technology. A brilliant architecture maintained by a fragile organization is a poor investment. A pragmatic system built by a capable team that understands its tradeoffs is compelling. Technical due diligence ultimately asks whether this team can execute the technical work required to achieve the business outcomes the investment assumes.
The metrics that answer this question are not the ones most engineering teams track. They are the outcomes those metrics allegedly predict: reliable service delivery, efficient resource use, sustainable team growth, rapid response to problems. Companies that focus on these outcomes rather than their proxies find technical due diligence less onerous and more productive. They also tend to build better systems, which is not a coincidence.