The Startup Technical Due Diligence Checklist

I've sat on both sides of the technical due diligence table—as a CTO defending my team's work, and as a technical advisor evaluating startups for investment. The experience taught me that most due diligence processes are broken. Investors ask the wrong questions, CTOs prepare the wrong materials, and both sides walk away with incomplete pictures.

This guide covers what a thorough technical due diligence should evaluate, what red flags actually matter versus cosmetic concerns, and how CTOs can prepare their teams to pass scrutiny without wasting weeks on theater.

What Technical Due Diligence Actually Assesses

DD isn't a code review. An investor spending $5-50M doesn't care about your variable naming conventions. They care about five fundamental questions:

1. Can this technology scale to support the business plan? (Architecture)
2. Is the team capable of executing the roadmap? (Team)
3. Are there hidden liabilities that could blow up? (Risk)
4. How efficiently does the team convert dollars into product? (Efficiency)
5. Is the technology defensible? (Moat)

Every question in due diligence maps back to one of these five concerns. If you're preparing for DD, organize your materials around them.

The Assessment Framework

Category 1: Architecture and Scalability

Assessment Area	What to Evaluate	Green Flag	Red Flag
System design	Architecture matches current and 12-month scale	Clearly documented, appropriate for stage	Over-engineered for 100x or under-built for 2x
Data architecture	Data model supports product roadmap	Clean schema, migration strategy exists	Massive technical debt in data layer
API design	External and internal APIs	Versioned, documented, backward-compatible	Breaking changes, no versioning
Infrastructure	Cloud resource management	IaC, automated scaling, cost-aware	Manual deployments, no scaling strategy
Performance	System under current and projected load	Load tested, headroom documented	No load testing, unknown breaking points

Key questions to ask:

• "Show me your architecture diagram and walk through a request lifecycle."
• "What happens to your system at 10x current traffic? At 100x?"
• "What's the most architecturally complex feature on your 12-month roadmap, and how does your current system support it?"
• "What would you rebuild if you started today?"

What to look for in answers: Honest self-awareness matters more than perfect architecture. A CTO who says "we made this tradeoff intentionally because X" is far more trustworthy than one who claims everything is perfectly designed.

Category 2: Team and Engineering Culture

Assessment Area	What to Evaluate	Green Flag	Red Flag
Team composition	Skills coverage for roadmap	Balanced levels, key roles filled	Critical single-person dependencies
Engineering process	Development lifecycle	Documented, iterative, improving	Ad hoc, no retrospectives
Knowledge distribution	Bus factor for critical systems	Cross-trained, documented	1-2 people hold all knowledge
Hiring ability	Pipeline and employer brand	Active pipeline, <45 day time-to-fill	Struggling to hire, >90 day open roles
Retention	Voluntary attrition	<15% annually	>25% or recent cluster departures

Key questions to ask:

• "If your three most senior engineers left tomorrow, what breaks permanently?"
• "Walk me through how a feature goes from idea to production."
• "How do you decide what to build vs what to buy?"
• "What's your biggest hiring challenge in the next 12 months?"

What to look for: Ask to speak with 2-3 engineers (not just the CTO). A healthy team has engineers who can articulate the product vision and technical strategy. If only the CTO can explain the roadmap, knowledge isn't distributed.

Category 3: Risk Assessment

Risk Category	What to Evaluate	Critical Red Flag
Security	Auth, encryption, access control, vulnerability management	No security audit ever conducted, plaintext secrets
Compliance	Regulatory requirements (GDPR, SOC 2, HIPAA, PCI)	Handling regulated data without appropriate controls
IP ownership	Code ownership, contractor agreements, open source licenses	GPL dependencies in proprietary code, unsigned IP assignments
Data integrity	Backup, recovery, data consistency	No tested backup strategy, no recovery procedure
Vendor lock-in	Dependency on third-party services	Single vendor with no migration path, >40% of infra on deprecated service
Technical debt	Volume and trajectory	Growing faster than revenue, blocking key roadmap items

Key questions to ask:

• "When was your last security audit? What did it find?"
• "Show me your backup and disaster recovery procedure. When did you last test it?"
• "What open source licenses are in your dependency tree? Have you audited for GPL?"
• "What third-party services could shut you down tomorrow if they went offline or changed pricing?"

Category 4: Efficiency and Velocity

Metric	Healthy (Series A-B)	Concerning
Deploy frequency	5-20+ per day	<1 per day
Change failure rate	<10%	>25%
Lead time for changes	<1 week	>1 month
Mean time to recovery	<1 hour	>4 hours
Feature cycle time	1-3 weeks	>6 weeks
Infrastructure cost / revenue	8-15%	>25%
Engineering cost / feature	$30K-$70K	>$120K

Key questions to ask:

• "How often do you deploy to production? Walk me through the process."
• "What's your average time from commit to production?"
• "How many features did you ship last quarter vs your plan?"
• "What's your infrastructure cost and how does it trend relative to revenue?"

Category 5: Technical Moat

Moat Type	Description	Evaluation Criteria
Data moat	Proprietary datasets that improve the product	Unique data, growing with usage, hard to replicate
Algorithm moat	Proprietary algorithms or models	Measurable advantage over generic solutions
Integration moat	Deep integrations that create switching costs	Complex integrations that took significant time to build
Network effects	Technology that gets better with more users	Each user's data improves experience for all users
Platform moat	Infrastructure others build on	Third-party developers or partners building on your platform

Key questions to ask:

• "What would it cost a well-funded competitor to replicate your technology from scratch?"
• "What technical advantage do you have that grows over time?"
• "How does your technology get better as you acquire more customers?"

The Scoring Model

I score each category on a 1-5 scale:

Score	Meaning	Investment Implication
5	Exceptional	Competitive advantage, adds to valuation
4	Strong	No concerns, supports business plan
3	Adequate	Normal for stage, manageable weaknesses
2	Concerning	Requires investment plan, potential negotiation point
1	Critical	Deal risk, may block investment without remediation plan

A strong Series A company should average 3.0-3.5 across all categories. A Series B company should average 3.5-4.0. Anything below 2.5 average needs a clear remediation plan before investment.

What Doesn't Matter (But CTOs Worry About)

Code quality perfection. No startup has perfect code. Investors expect trade-offs. What matters is whether the team knows where the debt is and has a plan.

Cutting-edge technology choices. Using Rails instead of Rust isn't a red flag. Using a stable, well-understood stack is often a green flag—it means the team prioritizes shipping over technology tourism.

100% test coverage. Coverage numbers are meaningless without context. 30% coverage on critical paths is better than 90% coverage testing getters and setters.

Documentation completeness. Architecture decision records and system diagrams matter. API documentation for internal tools doesn't.

Preparing for DD as a CTO

Start preparing 3-6 months before you expect the process to begin:

Month 1-2: Run your own DD checklist. Score yourself honestly. Identify the 2-3 areas that would score below 3.

Month 3-4: Address critical gaps. This usually means: document architecture, run a security audit, fix IP assignment paperwork, and ensure backups are tested.

Month 5-6: Prepare your materials package:

• Architecture overview (1-2 pages with diagrams)
• Team org chart with tenure and key responsibilities
• Deployment and development process documentation
• Infrastructure cost breakdown and trend
• Security audit results and remediation status
• IP assignment confirmation for all contributors
• DORA metrics dashboard (deploy frequency, lead time, MTTR, change failure rate)

During DD itself: Be honest. Investors respect CTOs who say "this is a known weakness and here's our plan" far more than those who pretend everything is perfect. The DD team will find the problems anyway—being upfront about them builds trust.

Actionable Takeaways

1. DD evaluates five things: scalability, team quality, risk, efficiency, and moat. Organize your preparation around these five pillars.
2. Red flags that kill deals: unassigned IP, zero security posture, critical single-person dependencies, no disaster recovery, GPL contamination.
3. Red flags that don't kill deals: imperfect code, technical debt (if quantified with a plan), legacy architecture (if migration path exists).
4. Start preparing 6 months early. The hardest problems to fix (IP assignment, security audit, bus factor reduction) take months.
5. Be honest about weaknesses. Every startup has them. Investors invest in teams that demonstrate self-awareness and a credible improvement plan.

Technical due diligence isn't an exam you pass or fail. It's a conversation about whether your technology supports the business vision. Approach it with transparency, preparation, and confidence in your team's ability to execute—and you'll come out stronger regardless of the outcome.