Technical Due Diligence for Investors: What We Look For in Code

Every investment has technical risk. The SaaS platform that looks polished in the demo might be running on a monolithic codebase with zero test coverage and a single developer who holds all the knowledge. The AI startup with impressive benchmarks might have hardcoded their evaluation metrics and built their model on a dataset they do not have the rights to use. The marketplace with strong unit economics might have a database schema that will collapse at ten times their current transaction volume. Technical due diligence exists to surface these risks before the term sheet is signed.

At LockedIn Labs, we conduct technical due diligence engagements for venture capital firms, private equity funds, and strategic acquirers. Over the past three years, we have evaluated more than 80 companies across fintech, healthtech, enterprise SaaS, and developer tools. This article shares our assessment framework — what we look for, what we flag as risk, and what separates engineering organizations that can scale from those that will hit a wall.

The Assessment Framework: Six Dimensions of Technical Health

We evaluate every company across six dimensions, each scored on a five-point maturity scale. The dimensions are not weighted equally — their importance varies by company stage, market, and the specific investment thesis. A Series A startup gets different scrutiny than a pre-acquisition target. But the framework itself is consistent, which lets us compare across engagements and track how companies evolve over time.

Architecture: Can This System Handle 10x Growth?

The first question we ask about architecture is not whether it is good or bad in the abstract, but whether it is appropriate for the company’s current stage and its growth trajectory. A well-structured monolith serving 10,000 users is not a red flag. A poorly decomposed microservices architecture at the same scale is — it suggests the team optimized for future complexity before validating the core product, which is a judgment error that compounds over time.

We look for clear separation of concerns, not necessarily microservices. Can the database schema evolve without rewriting the application? Can the frontend deploy independently of the backend? Are external dependencies abstracted behind interfaces that can be swapped? These structural decisions determine how fast the team can move when the product evolves. A tightly coupled system where changing the payment provider requires modifications in 30 files across 5 services is a velocity tax that compounds with every new feature.

Scalability is assessed against the investment thesis. If the thesis assumes the company will grow from 10,000 to 1 million users in 18 months, we need to see either an architecture that can handle that load or a clear, credible path to get there. The most common scalability blockers we find are single-database architectures with no sharding strategy, synchronous processing pipelines that should be async, and stateful application servers that cannot be horizontally scaled. None of these are fatal at Series A, but they become existential risks at Series C if the team has not planned for them.

Code Quality: Reading Between the Lines

Code quality tells you about the engineering team’s values and discipline. We do not use automated quality scores as the primary signal — they are useful for identifying outliers but miss the nuances that matter most. Instead, we conduct deep-dive reviews of four to six critical code paths: the authentication flow, the core business logic, the payment or billing system, the data pipeline, and any area the team identifies as technically challenging. We read the code like an experienced engineer joining the team would read it, asking: can I understand what this does? Can I modify it safely? Can I trust it with sensitive data?

Test coverage is a useful signal when interpreted correctly. We care less about the coverage percentage and more about what is tested. A codebase with 40% coverage that tests every critical business rule and edge case is healthier than one with 90% coverage that only tests happy paths. We look for integration tests that verify the system works end-to-end, not just unit tests that verify individual functions in isolation. We look for tests that document the team’s understanding of edge cases — a test named “should handle concurrent refund requests on the same order” tells us the team has thought about real-world failure modes.

Error handling separates professional engineering from prototype code. We look for consistent error handling patterns, proper use of error types, meaningful error messages, and graceful degradation. A codebase littered with empty catch blocks, generic error messages, or functions that silently swallow failures is a maintenance burden that grows exponentially. Security patterns matter as well: parameterized queries, input validation at the boundary, proper secret management, and secure defaults. A SQL injection vulnerability in a fintech application is not just a technical debt item — it is a material investment risk.

Technical Debt: The Hidden Balance Sheet

Every codebase has technical debt. The question is whether the team manages it intentionally or whether it has accumulated through neglect. We distinguish between strategic debt — deliberate shortcuts taken to ship faster with a clear plan to address them — and unmanaged debt — accumulated complexity that nobody planned for and nobody owns. Strategic debt is normal and healthy at early stages. Unmanaged debt is a risk that often requires significant post-investment remediation.

We quantify technical debt through several signals. Dependency freshness: how many dependencies are more than two major versions behind? Outdated dependencies are not just a maintenance problem — they accumulate known vulnerabilities. TODO density: how many TODO and FIXME comments exist in the codebase, and how old are they? A codebase with hundreds of year-old TODOs suggests the team does not have a process for managing follow-up work. Build time: how long does the CI pipeline take? Build times over 20 minutes actively slow development velocity and correlate with deferred infrastructure investment.

Team and Process: The Human Factor

Architecture and code quality are lagging indicators of the team’s engineering culture. The team and process assessment is our leading indicator — it predicts where the codebase will be in twelve months better than the current state of the code. We evaluate the team through interviews, not just code review. We talk to the CTO, the senior engineers, and at least one junior or mid-level engineer. The gap between the CTO’s description of their engineering practices and the junior engineer’s experience of those practices is often the most informative signal in the entire assessment.

Knowledge distribution is a critical risk factor. We look at the git commit history to understand who has modified which parts of the codebase. If one engineer accounts for 80% of commits in a critical subsystem, that is a bus factor risk. If the commit frequency drops off outside business hours and there are no commits from engineers in other time zones, it tells us about the team’s capacity for follow-the-sun support. If pull request reviews are perfunctory — approved within minutes with no comments — it suggests a culture where code review is a formality rather than a quality gate.

We also assess the development workflow end to end: how does a feature go from idea to production? The best teams we evaluate have a clear, documented workflow with automated guardrails — feature branches, required code review, CI checks that must pass before merge, automated deployment to staging, manual promotion to production. The worst have a workflow that exists only in the CTO’s head, with code pushed directly to the main branch and deployed manually.

The Deliverable: What Investors Get From Us

Our due diligence reports are structured for decision-makers, not engineers. The executive summary leads with the bottom line: invest, invest with conditions, or pass. The risk matrix maps every finding to its impact (low, medium, high, critical) and its remediation cost (quick fix, moderate effort, significant investment). We estimate the total remediation cost in engineering months and dollars so investors can factor it into their valuation model.

We also provide a 90-day roadmap for the most critical findings. This serves two purposes: it gives the investor confidence that the risks are addressable, and it gives the portfolio company a concrete action plan if the investment proceeds. The roadmap is sequenced by risk priority, not engineering preference — the items that could cause a production outage or a compliance violation come first, regardless of how interesting the architecture improvements are.

Technical due diligence is not about finding perfect code — it does not exist. It is about understanding the technical risk profile of an investment and ensuring that the engineering team has the capability and the culture to address identified issues. The best companies we evaluate do not have zero debt or perfect architecture. They have teams that understand their technical landscape, manage their debt intentionally, and build systems that can evolve as the business grows. That is what we look for, and that is what we report on.