Technical Due Diligence for Investors: What to Check Before Investing

Why investors need a technical X-ray

Forty-two percent of technology acquisitions that miss their synergy targets fail due to technical issues undetected during due diligence. That figure comes from Bain & Company, 2024, and it surprises nobody who has looked inside a Series A startup with a functional product and a codebase that would not survive six months of growth.

Financial and legal due diligence are mature practices. Technical due diligence, in many deals, remains a surface-level review: a glance at the tech stack, a question about uptime, and not much else. The result is investors discovering scalability problems, technical debt, or key-person dependency after they have signed.

This guide covers what we examine when a fund or family office asks us to evaluate a target company’s technology.

Five areas of review

We organize technical due diligence into five areas, each with quantifiable indicators and red flags.

1. Code quality and development practices

This is not about whether the code is “pretty.” It is about whether it is maintainable, testable, and secure.

Indicators we measure: test coverage (below 30% is a red flag), deployment frequency (less than once per month suggests fragile processes), mean time to resolution for critical bugs (over 5 days is concerning), and presence of linters, formatters, and CI/CD in the pipeline.

Tools we use: SonarQube for static analysis, Semgrep for security vulnerabilities, and manual review of a representative sample of the repository. The manual review is irreplaceable. An automated scan tells you there are 200 code smells. A senior engineer tells you which of those 200 matter and which are noise.

Critical red flag: total absence of automated tests. A codebase without tests is not just a quality issue; it is an indicator that the team cannot make changes with confidence, which slows product evolution.

2. Architecture and scalability

The central question is not “what is the current architecture” but “can the current architecture handle 10x current volume without a rewrite?”

We review: separation of concerns (a well-structured monolith is acceptable; a tightly coupled monolith is not), state management (where state lives, how it synchronizes), database strategy (indexes, partitioning, growth plan), and external dependencies (third-party APIs without fallbacks are single points of failure).

What we look for specifically: predictable bottlenecks. A SQL table with 50 million rows and no partitioning. A synchronous service calling an external API with a 30-second timeout on the main thread. A queue system that is actually a cron job polling every minute. These are not abstract problems; they will explode at a predictable scale.

In a recent due diligence for a Spanish fintech, we found that all payment reconciliation logic lived in a 2,400-line stored procedure in PostgreSQL. It worked. But if transaction volume doubled (which the business plan projected within 18 months), the database would not cope. We quantified the extraction cost: 3 months for a team of 2 engineers. That number directly affected the valuation.

3. Tech debt quantification

Technical debt is not inherently bad. It is bad when it is unquantified, unprioritized, and has no repayment plan.

We categorize debt at three levels:

• Critical debt: Issues that will block growth or cause incidents. Require intervention within 3-6 months. Examples: database without automated backups, hardcoded secrets in the repository, dependencies with known vulnerabilities.
• Significant debt: Issues that reduce team velocity or increase risk. Resolution plan within 6-12 months. Examples: untested modules that are frequently modified, fragile integrations, nonexistent API documentation.
• Acceptable debt: Conscious shortcut decisions that do not affect operations or near-term growth. Examples: use of legacy but stable libraries, duplicated code in low-change areas.

For each critical and significant debt item, we estimate remediation cost in person-weeks. This converts an abstract concept (“there is a lot of tech debt”) into a number an investor can plug into their financial model.

4. Security and compliance

Security is not a separate area; it is a cross-cutting dimension. But given its impact on investment risk, it deserves dedicated review.

Minimum checklist: secrets management (Vault, AWS Secrets Manager, or at minimum environment variables — never in code), HTTPS on all endpoints, authentication and authorization implemented (not just a login: role-based access control, tokens with expiration), encryption at rest for sensitive data, and GDPR compliance if operating in Europe (data protection officer, processing records, retention policy).

For fintech targets, the review extends to PCI DSS (if handling card data), PSD2 (if offering payment services), and MiCA regulation if dealing with crypto. Compliance gaps are not just operational risk; they are legal risk that can invalidate the investment.

Tools: Semgrep and Snyk for code and dependency vulnerabilities, a basic pentest (Burp Suite or equivalent) against public endpoints, and review of cloud infrastructure configuration (SecurityHub on AWS, Security Command Center on GCP).

5. Team and execution capability

Technology is built by people. We review: team size and structure (a CTO who is also the sole developer is a key-person risk), turnover in the past 12 months (above 30% annual is a red flag), contribution distribution in the repository (if 80% of commits come from one person, there is knowledge concentration), and hiring capacity (if they need to double the team in 12 months, is that feasible given their brand and location?).

A pattern we see frequently: startups with a 3-4 person tech team that built a functional product at impressive speed, but with development practices that only work at that scale. No documentation, no processes, no environment separation. What made them fast at 3 will make them slow at 15. The investment plan must include a professionalization period, and that has a cost the investor should know.

The deliverable: what the investor receives

Our technical due diligence report has a fixed structure:

1. Executive summary (1 page): overall verdict, critical risks, estimated remediation cost.
2. Area-by-area evaluation (5 sections): each area scored 1-5, with findings, evidence, and recommendations.
3. Risk matrix: probability vs. impact for each critical and significant finding.
4. Remediation cost estimate: in person-weeks and euros, with ranges.
5. Open questions: issues requiring additional information not available during the review.

Typical timeline is 2-3 weeks, depending on codebase size and the target team’s cooperation. Cost ranges from EUR 8,000 to 25,000 depending on scope.

When technology changes the valuation

In our experience, roughly 30% of due diligences reveal problems that adjust the valuation downward. They do not necessarily kill the deal: sometimes the product and market justify the investment even with significant tech debt. But the investor goes in with open eyes and a budgeted remediation plan.

The other 70% confirm that the technology is solid or has minor issues. That confirmation also has value: it eliminates uncertainty and gives the investment committee confidence.

If you are evaluating an investment in a company with a technology component, our technology consulting team delivers actionable due diligence reports. Technical due diligence is not a nice-to-have. It is the difference between investing with data and investing with hope.