Technical due diligence is increasingly standard for Series A and later rounds. Some seed investors are starting to request it too. Here is exactly what investors check, what good looks like, and how to prepare — so the technical review strengthens your raise instead of killing it.
What Investors Actually Evaluate
Area 1: Code Quality
What they check:
- Consistent coding standards (formatting, naming conventions)
- Reasonable test coverage (they do not expect 100%, but 0% is a red flag)
- Code review history (are PRs reviewed before merging?)
- Commit history (logical commits vs. "fix" "fix again" "please work")
- Dependency management (are dependencies up to date? Known vulnerabilities?)
What good looks like:
- Automated formatting and linting (consistent code style without effort)
- 50-70% test coverage on critical paths (auth, billing, core workflows)
- All PRs have at least one reviewer
- Clean commit history (squashed or logical commits)
- No critical dependency vulnerabilities
What raises red flags:
- No tests at all
- Single developer merging their own PRs without review
- Massive files with 1,000+ lines and no modular structure
- Dependencies with known critical vulnerabilities
- Secrets (API keys, passwords) found in the codebase
Area 2: Architecture
What they check:
- Is the architecture appropriate for current and projected scale?
- Are there clear boundaries between system components?
- Is the database schema well-designed?
- Are there single points of failure?
- Is the architecture documented?
What good looks like:
- Modular structure (even within a monolith)
- Database schema with proper indexes, foreign keys, and normalization
- Caching strategy for frequently accessed data
- Separation of concerns (API layer, business logic, data layer)
- Architecture diagram that someone outside the team can understand
What raises red flags:
- Business logic scattered throughout the codebase (no clear architecture)
- Database with no indexes on frequently queried columns
- No separation between application tiers
- Architecture that cannot handle 10× current load without rewriting
Area 3: Security
What they check:
- Authentication and authorization implementation
- Data encryption (at rest and in transit)
- Input validation and sanitization
- Secret management (how API keys and credentials are stored)
- Compliance with relevant regulations (GDPR, HIPAA, SOC 2)
- Vulnerability scanning (dependencies and infrastructure)
What good looks like:
- Authentication using established libraries (not custom implementations)
- HTTPS everywhere, data encrypted at rest
- Input validation on all user-facing endpoints
- Secrets in environment variables or secret managers (not in code)
- Regular dependency updates
- Privacy policy and data handling documentation
What raises red flags:
- Custom authentication implementation (high risk of vulnerabilities)
- Passwords stored in plain text (hard to believe, but it happens)
- API keys committed to the repository
- No input validation (SQL injection, XSS vulnerabilities)
- No HTTPS on production
- No awareness of relevant compliance requirements
Area 4: Team Capability
What they check:
- Team composition (seniority distribution, skill coverage)
- Development process maturity (sprints, code review, CI/CD)
- Technical decision-making process (who decides, how are decisions documented)
- Knowledge distribution (bus factor — what happens if one person leaves)
- Hiring pipeline (can the team grow?)
What good looks like:
- Mix of senior and mid-level engineers
- Clear sprint process with regular demos and retrospectives
- Code review and CI/CD as standard practice
- Architecture decisions documented
- No single person who is the only one understanding critical systems
What raises red flags:
- Entire engineering knowledge in one person's head
- No development process (code goes directly from editor to production)
- Team cannot articulate why architectural decisions were made
- Unable to demonstrate recent sprint velocity or delivery cadence
- All developers are junior with no senior oversight
Area 5: Scalability
What they check:
- Can the current architecture handle projected growth?
- Are there obvious bottlenecks?
- What is the scaling plan (horizontal vs vertical, infrastructure choices)?
- What are the infrastructure costs at 10× and 100× current usage?
- Is there monitoring and alerting in place?
What good looks like:
- Stateless application servers (can add more instances)
- Database can scale vertically for the next 12-18 months
- Clear plan for when vertical scaling is insufficient
- Infrastructure-as-code (reproducible deployments)
- Basic monitoring (uptime, error rates, response times)
- Cost projections for infrastructure at scale
What raises red flags:
- Application state stored in memory (cannot scale horizontally)
- No monitoring (team does not know when things break)
- Infrastructure configured manually (not reproducible)
- No understanding of current infrastructure costs or scaling trajectory
- Architecture that requires a complete rewrite to handle 10× growth
How to Prepare
4 Weeks Before Diligence
Week 1: Self-Assessment
- Run a security scan (Snyk, npm audit, or equivalent)
- Check for secrets in the repository (use git-secrets or trufflehog)
- Review test coverage numbers
- Update critical dependencies
Week 2: Documentation
- Create or update architecture diagram
- Document the top 10 technical decisions and their reasoning
- Write a one-page infrastructure overview
- Prepare a team capability summary
Week 3: Fix Critical Issues
- Remove any secrets from the codebase (and rotate them)
- Fix critical security vulnerabilities
- Add basic tests for untested critical paths
- Set up basic monitoring if none exists
Week 4: Review and Polish
- Have someone outside the team review the documentation
- Prepare answers for common due diligence questions
- Create a demo environment that showcases the system running
- Brief the team on what to expect during technical interviews
What You Cannot Fake
Investors who conduct serious technical diligence will see through surface-level preparation:
- Adding tests in the final week that do not actually test meaningful behavior
- Documentation that describes the ideal architecture, not the actual one
- Clean-up commits that hide the true state of the codebase (git history is visible)
Be honest about current limitations and articulate a plan for addressing them. Investors expect early-stage code to have rough edges. What they do not expect is ignorance about where those rough edges are.
The Fractional CTO Advantage
A fractional CTO who has been working with your company for even 2-3 months can:
- Conduct the self-assessment with credibility
- Prepare documentation based on genuine understanding
- Address critical issues as part of ongoing work (not last-minute cramming)
- Represent technical capability in investor conversations
- Answer due diligence questions from a position of knowledge
The cost of 3 months of fractional CTO services ($9K-$15K) is a fraction of the funding at risk if technical diligence goes poorly.