Series A investors write large checks into companies where the product is working and the business is de-risked. Part of de-risking is technical due diligence — an assessment of whether the engineering that produced the traction is built on a foundation that can scale, or whether the growth you've demonstrated will require a complete rewrite to sustain.
Technical due diligence is not about whether the code is perfect. It's about whether the code is honest. A system with rough edges that the team understands and has a plan for is fundable. A system where the team doesn't understand the rough edges — or where the rough edges are structural — creates problems.
Here's what due diligence actually covers and how to prepare.
What Technical Due Diligence Is and Isn't
What it is: An independent assessment of your engineering practices, codebase quality, infrastructure, security posture, and team capability. Conducted by a technical partner at the fund, an outside engineering firm, or a senior engineer the fund trusts.
What it isn't: A code review that finds and fixes bugs. Due diligence reviewers are not there to improve your system. They're there to characterize the risk.
The output is typically a memo that answers: what did we find, what are the risks, are they manageable, and does the team have the capability to address them?
The memo goes to the investment committee. A positive DD memo is not a guarantee of a term sheet. A negative DD memo is often a deal-killer.
What Investors Actually Look For
Codebase Quality and Architecture
Reviewers read code. Not all of it — they sample strategically, starting with the core business logic and the data model. The signals they look for:
Is the system understandable? Code that requires extensive explanation is a risk. Complex logic that isn't encapsulated, inconsistent naming conventions, missing or misleading variable names — all signal a team that's building faster than it's thinking.
Is the data model sensible? Database schema is the most expensive thing to change after launch. A data model that doesn't reflect the domain, that conflates different concepts into single tables, or that has no foreign keys and relies entirely on application-level relationships is a warning sign.
Is there test coverage? Not perfection — most early-stage codebases don't have comprehensive test coverage. But zero tests on critical paths (payment processing, auth, data mutations) signals that the team isn't thinking about correctness.
What's the technical debt density? Some technical debt is normal and expected at Series A. The question is whether it's documented and managed, or whether it's invisible and growing.
Infrastructure and Scalability
Reviewers assess whether the infrastructure is appropriate for current and projected load:
Can it handle 10x your current traffic? The question isn't whether it can handle 10x today — it probably can't, and that's fine. The question is whether there's a credible path to getting there and whether the team understands what the bottlenecks are.
Are there single points of failure? A production system with a single database instance, no read replicas, and no failover is a risk. A database failure at peak traffic should be a degraded experience, not a complete outage.
How does deployment work? Manual deployments — SSH into a server, run a script — are a red flag. Not because they're inherently wrong, but because they don't scale with team size and create a single point of failure in the release process. CI/CD pipelines with staging environments and rollback capability are expected at Series A.
What's the incident response process? "We get a call from a customer" is not an incident response process. Monitoring, alerting, on-call rotation, and a post-incident review process signal engineering maturity.
Security Posture
Security findings are taken seriously because security incidents at early-stage companies have outsized damage to customer trust. Key checks:
Secrets management. Are API keys and credentials hardcoded in source code, committed to version control, or stored in unencrypted environment files? This is one of the most common findings in Series A due diligence. The fix is a secrets manager (AWS SSM, HashiCorp Vault, GCP Secret Manager) and rotation on a schedule.
Authentication and authorization. Is authentication implemented correctly? Are there broken access control bugs — endpoints that return data without checking whether the requesting user is authorized to see it? This class of bug is extremely common in products built fast under pressure.
Dependencies. Running outdated packages with known vulnerabilities is a risk. npm audit or pip-audit with a list of critical findings and no remediation plan is a yellow flag.
Data encryption. Is sensitive user data encrypted at rest and in transit? Is the database connection encrypted? For regulated data (PHI, PII, financial data), reviewers will specifically look for encryption controls.
Engineering Team Capability
DD reviewers form a view on the team as part of their assessment. They're evaluating:
Does the team understand the system? Engineers who can explain their architectural decisions, including the tradeoffs they made and the debt they incurred, demonstrate ownership. Engineers who say "the previous developer did that" for core system components are concerning.
Is the team concentrated in a single person? A system where one engineer holds all the knowledge is a key-person risk. If that engineer leaves, can the company continue to ship? Reviewers look for documentation, reasonable code organization, and the team's ability to answer questions across the codebase.
What's the team's velocity? Deployment frequency, lead time for changes, and how long it takes to ship a new feature give a signal on engineering velocity. A team that deploys once a week is much lower velocity than one that deploys multiple times per day.
Findings That Kill Deals
Not all issues are equal. Some findings are manageable risks; others are deal-breakers.
Architectural impossibilities. A system that cannot scale to the projected user count without a fundamental rewrite — not an optimization, a rewrite — is a major problem. Reviewers distinguish between "this needs engineering work" and "this needs to be rebuilt." The latter has a significant impact on how investors value the funding need.
Security vulnerabilities in critical paths. A broken access control bug that would let any user see any other user's data is a deal-killer until fixed. SQL injection vulnerabilities in the payment flow are a deal-killer. These are not yellow flags — they require a fix before the deal can proceed.
Hidden technical debt with no plan. Technical debt that the team doesn't know about or hasn't prioritized is riskier than technical debt with a remediation plan. Reviewers who find significant issues and then find that the team is unaware of them conclude that the team doesn't have good visibility into their own system.
Dependency on deprecated or abandoned technologies. A core dependency on an unmaintained open-source library, a database version that's end-of-life, or a cloud service that's being sunset creates a forced migration with no warning. This signals insufficient attention to the long-term sustainability of technology choices.
Key-person risk without mitigation. A team of five where only one person understands the core AI model, the database schema, and the authentication system is existential risk. Due diligence reviewers flag this explicitly.
How to Prepare Before the Process Starts
The best time to prepare for technical due diligence is 6–12 months before you expect to run a Series A.
Get an independent technical review. A technical review from an external engineering team — not your own engineers — gives you the same picture the DD reviewer will see. Issues found in an internal review can be fixed. Issues found in DD require real-time explanation and may create deal uncertainty.
Fix the obvious things. Secrets in source code, outdated dependencies with critical CVEs, broken access control on obvious endpoints — these are mechanical fixes. A week of engineering time addressing known issues before DD begins is worth far more than the same time after a term sheet is contingent on remediation.
Document your technical debt. A documented list of known issues with a prioritized remediation plan is far less concerning than undocumented issues discovered in DD. Reviewers who find a "tech debt backlog" in your issue tracker see a team that's managing its risks. Reviewers who find the same issues but no evidence the team knew about them see a team with low visibility.
Define your incident response process. Even a simple on-call rotation with a documented escalation path and a post-incident review template shows maturity. It doesn't need to be complex — it needs to exist.
Get your monitoring in place. Error rates, latency, database performance, and deployment frequency should all be tracked and visible. A Grafana dashboard and alert configuration demonstrates that you operate your system with intention.
Getting Ready for Series A
The technical readiness assessment is one part of Series A preparation. The engineering practices that give you a clean DD report — CI/CD, secrets management, observability, test coverage, sensible architecture — are the same practices that make your team faster and your product more stable.
Our DevOps services practice runs technical audits that cover exactly what Series A reviewers look at: pipeline run times, infrastructure gaps, secrets posture, observability coverage, and deployment frequency. The output is a prioritized remediation roadmap.
For teams building the product that needs to be fundable, our web app development practice builds with Series A readiness in mind from the first sprint — architecture documentation, test coverage on critical paths, CI/CD from day one, and no secrets in source code.
For AI-first products where DD will include an assessment of model quality and MLOps maturity, our AI services team builds production systems with evals, drift detection, and cost controls — the same evidence a technical reviewer wants to see.
Book a 30-min discovery call — we scope a technical assessment of your current system, identify the highest-risk findings, and quote a fixed price for remediation before your next fundraise.