Technical Due Diligence for Startup Fundraising: What Investors Actually Evaluate and How to Prepare Your Codebase, Architecture, and Team for Scrutiny

Technical due diligence (tech DD) is the systematic evaluation of a startup's technology assets, architecture, team capabilities, and technical risks. For Series A and beyond, nearly every institutional investor conducts some form of tech DD. For acquisitions, it is universal and exhaustive. Understanding what evaluators look for — and preparing proactively — can mean the difference between closing a round at your target valuation and watching the deal collapse over fixable technical issues.

When Technical Due Diligence Happens

Pre-Seed and Seed

Tech DD at this stage is minimal and informal. Investors evaluate the founding team's technical credibility, check that the product works, and look for obvious red flags (no version control, no deployment pipeline, single point of failure).

Series A

This is where formal tech DD typically begins. Investors may bring in external technical advisors or in-house CTOs to evaluate architecture, code quality, scalability approach, and team structure. Expect 1-2 weeks of evaluation.

Series B and Beyond

Tech DD becomes comprehensive. Evaluators will review code repositories, infrastructure architecture, security practices, data management, technical debt, team composition, and development processes. Expect 2-4 weeks.

Acquisition

The most thorough DD process. Acquirers may review every aspect of your technology stack, IP ownership, open-source license compliance, security vulnerabilities, data handling practices, and team retention risk. Expect 4-8 weeks.

The Seven Dimensions of Technical Due Diligence

1. Architecture and Scalability

What evaluators look for:

• Can the current architecture handle 10x the current load without a rewrite?
• Are there single points of failure that could cause outages?
• Is the architecture documented and understandable by new engineers?
• Are there clear separation of concerns (frontend, backend, data, infrastructure)?

Common red flags:

• Monolithic architecture with no path to decomposition
• No load testing or capacity planning
• Database queries that do not scale (N+1 queries, missing indexes, full table scans)
• Hardcoded configuration values scattered throughout the codebase
• No caching strategy for frequently accessed data

How to prepare:

• Create an architecture diagram showing all services, databases, APIs, and external dependencies
• Document your scaling strategy (horizontal vs. vertical, when each applies)
• Run basic load tests and document results
• Identify and document known scaling bottlenecks and your plan to address them

2. Code Quality and Technical Debt

What evaluators look for:

• Is the codebase maintainable? Could a new engineer onboard within 2 weeks?
• How much technical debt exists, and is there a plan to address it?
• Are there automated tests with reasonable coverage?
• Is the codebase consistent in style, structure, and patterns?

Common red flags:

• No automated tests or test coverage below 20%
• Large files with thousands of lines and no clear structure
• Copy-pasted code across multiple locations
• Commented-out code blocks left in production
• No linting or code formatting standards
• Direct database queries in API handlers (no service/repository layer)

How to prepare:

• Measure test coverage and aim for 60%+ on critical paths (not necessarily 90% overall)
• Implement linting and formatting (ESLint, Prettier, Black — whatever fits your stack)
• Address the worst technical debt items (the ones that slow down every sprint)
• Remove dead code, commented-out blocks, and unused dependencies
• Write README files for each major component explaining its purpose and structure

3. Security Practices

What evaluators look for:

• Are there obvious security vulnerabilities (SQL injection, XSS, unencrypted secrets)?
• Is authentication and authorization properly implemented?
• How is sensitive data stored and transmitted?
• Is there an incident response plan?

Common red flags:

• Secrets (API keys, database passwords) committed to version control
• No HTTPS enforcement
• Passwords stored without hashing (or with weak hashing like MD5)
• No rate limiting on authentication endpoints
• SQL injection vulnerabilities in database queries
• No dependency vulnerability scanning
• Admin functions accessible without proper authorization

How to prepare:

• Run a secret scanning tool (git-secrets, truffleHog) on your repository history
• Implement dependency vulnerability scanning (Dependabot, Snyk free tier)
• Ensure all API endpoints have proper authentication and authorization
• Document your security practices (even a simple 1-page security overview helps)
• Fix any critical vulnerabilities identified by automated scanning

4. Infrastructure and DevOps

What evaluators look for:

• Is infrastructure defined as code (reproducible, version-controlled)?
• Is there a CI/CD pipeline for automated testing and deployment?
• How are deployments handled? Can you roll back quickly?
• What is your uptime and how do you monitor it?

Common red flags:

• Manual deployments (SSH into servers and running commands)
• No staging/testing environment (deploying directly to production)
• No monitoring or alerting for production issues
• Infrastructure not reproducible (snowflake servers configured by hand)
• No backup strategy or untested backups

How to prepare:

• Implement CI/CD (GitHub Actions, GitLab CI — free tiers are sufficient)
• Set up basic monitoring (uptime, error rates, response times)
• Create a staging environment that mirrors production
• Document your deployment process and rollback procedure
• Verify that backups work by actually restoring from a backup

5. Data Management and Analytics

What evaluators look for:

• How is data stored, organized, and accessed?
• Is there a data pipeline for analytics and reporting?
• How is data quality maintained?
• What is the data retention and deletion strategy?

Common red flags:

• No database migrations (schema changes applied manually)
• No data backup or recovery strategy
• Personally identifiable data stored without encryption or access controls
• No analytics pipeline (decisions made without data)
• Data scattered across multiple unconnected systems with no single source of truth

6. Team and Development Process

What evaluators look for:

• Does the team have the right skills for the current and next stage?
• Is there a structured development process (sprints, code reviews, documentation)?
• What is the team's velocity and how is it tracked?
• Are there key-person dependencies (critical knowledge in one person's head)?

Common red flags:

• No code review process
• Single engineer with no documentation (bus factor of 1)
• No project management tool or process
• High turnover in the engineering team
• Founder-only engineering with no plan to hire

7. Intellectual Property and Open-Source Compliance

What evaluators look for:

• Does the company own all the code in its repository?
• Are there proper IP assignment agreements with all engineers and contractors?
• Is open-source usage compliant with license terms?
• Are there any third-party code or library dependencies with problematic licenses (GPL in proprietary code)?

Common red flags:

• No IP assignment agreements with contractors who wrote significant code
• GPL-licensed code used in proprietary software without compliance
• Code copied from Stack Overflow or other sources without license consideration
• No open-source license inventory

The Pre-DD Audit Checklist

Run this checklist before any fundraising process begins:

Codebase Health

• Repository has clear README with setup instructions
• Codebase passes linting without errors
• Test coverage measured and above 40% on critical paths
• No secrets in version control history
• Dead code and unused dependencies removed
• Database migrations are version-controlled and reversible

Infrastructure

• CI/CD pipeline runs tests and deploys automatically
• Staging environment exists and mirrors production
• Basic monitoring and alerting in place
• Backup strategy documented and tested
• Deployment rollback procedure documented

Security

• Dependency vulnerability scan completed (zero critical/high findings)
• Authentication and authorization reviewed
• HTTPS enforced everywhere
• Secret management solution in place (not environment variables in code)
• Security incident response plan documented

Documentation

• Architecture diagram (current state)
• API documentation (at least for external-facing APIs)
• Development setup guide (new engineer can onboard in <1 day)
• Key technical decisions documented (ADRs or similar)

Legal/IP

• IP assignment agreements on file for all contributors
• Open-source license inventory completed
• No GPL code in proprietary codebase (or compliance verified)
• All third-party SaaS agreements documented

Common Deal-Killing Findings

These findings consistently cause deals to fall through or valuations to drop:

1. Secrets in git history — Even if removed from current code, secrets in commit history indicate poor security practices
2. No automated tests — Suggests the codebase is fragile and changes carry high risk
3. IP ownership gaps — Missing contractor agreements create legal uncertainty about code ownership
4. Scaling impossibility — Architecture that cannot handle 10x growth without a complete rewrite
5. Single point of failure — One engineer, one server, one database with no redundancy

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