Technical due diligence isn't about finding perfect code. It's about assessing risk. Acquirers want to understand what they're buying, what it will cost to maintain, and what surprises might emerge after the deal closes.

Many founders assume acquirers care most about cutting-edge technology or elegant architecture. They don't. They care about predictability, maintainability, and hidden liabilities. Here's what they actually evaluate.

The Due Diligence Mindset

Before diving into specifics, understand how acquirers think about code.

They're not looking for perfection. Every codebase has issues. Acquirers know this. They're assessing whether issues are manageable or disqualifying.

They're pricing risk. Every problem they find either reduces the offer price or adds to post-acquisition investment requirements. Major issues can kill deals entirely.

They're imagining post-acquisition. Can their team maintain this? Can they extend it? Will they need to rewrite it? How much will that cost?

They've seen many codebases. Experienced acquirers have patterns. They know what correlates with future problems. They're pattern-matching against past deals.

Code Quality and Standards

What They Check

Consistency: Does the codebase follow consistent patterns? Or does every file look like it was written by a different person with different standards?

Readability: Can a competent developer understand what the code does? Or is it cryptic, clever, and impenetrable?

Modern Practices: Is the code written using current best practices? Or is it using deprecated patterns, obsolete libraries, and outdated approaches?

Error Handling: How does the code handle failures? Gracefully with clear error messages? Or with crashes and silent failures?

What Raises Flags

• Inconsistent formatting and naming conventions
• Complex functions doing too many things
• Copy-pasted code throughout the codebase
• Commented-out code that nobody removed
• Magic numbers and hardcoded values
• Deeply nested logic that's hard to follow

What They Want to See

• Consistent code style (preferably enforced by tools)
• Clear, descriptive naming
• Functions that do one thing well
• Appropriate use of abstractions
• Code that's self-documenting where possible
• Comments that explain why, not what

Test Coverage

What They Check

Existence: Are there tests at all? You'd be surprised how often the answer is no.

Coverage: What percentage of code is tested? More importantly, are the critical paths covered?

Quality: Do tests actually test meaningful behavior? Or are they superficial checks that pass regardless of bugs?

Maintenance: Are tests kept up to date? Or are there failing tests that everyone ignores?

What Raises Flags

• No tests or minimal test coverage
• Tests that are frequently skipped or ignored
• High coverage numbers but shallow tests
• Flaky tests that fail intermittently
• Tests that haven't been updated as code changed

What They Want to See

• Meaningful test coverage of critical functionality
• Tests that run automatically on every change
• Tests that actually catch bugs when code breaks
• A culture of maintaining test quality
• Integration and end-to-end tests for key flows

Documentation

What They Check

Architecture Documentation: Is there a clear explanation of how the system works? What are the major components? How do they interact?

Setup Documentation: Can a new developer get the system running? How long does it take? What's missing?

API Documentation: Are interfaces documented? Can consumers understand how to use them?

Decision Records: Why were major decisions made? What alternatives were considered?

What Raises Flags

• No documentation at all
• Documentation that's clearly outdated
• Setup instructions that don't work
• Tribal knowledge required to operate the system
• No explanation of architectural decisions

What They Want to See

• Current architecture overview
• Working setup instructions
• API documentation that matches implementation
• Records of major technical decisions
• Onboarding guides that actually work

Dependencies and Technical Debt

What They Check

Dependency Health: Are dependencies maintained? Are there known security vulnerabilities? How outdated are the versions?

Framework Choices: Are frameworks still supported? Is the community active? Or are they using abandoned projects?

Technical Debt: How much cleanup is needed? Are there known issues being deferred? What would modernization require?

Upgrade Path: Can dependencies be upgraded? Or are there blockers preventing security patches?

What Raises Flags

• Dependencies with known security vulnerabilities
• Outdated frameworks with no upgrade path
• End-of-life language versions
• Vendor lock-in with unfavorable terms
• Technical debt that would require significant investment

What They Want to See

• Current, maintained dependencies
• Clear upgrade paths for major components
• Understood and documented technical debt
• Reasonable dependency choices
• No critical security vulnerabilities

Security Posture

What They Check

Authentication and Authorization: How are users authenticated? How are permissions managed? Are there vulnerabilities?

Data Protection: How is sensitive data handled? Is it encrypted? Are there exposure risks?

Input Validation: Is user input validated? Are there injection vulnerabilities?

Security History: Have there been breaches? How were they handled? What was learned?

What Raises Flags

• Hardcoded secrets in code or configuration
• Unencrypted sensitive data
• SQL injection or XSS vulnerabilities
• No security testing or auditing
• History of breaches without clear remediation

What They Want to See

• Proper secret management
• Encryption of sensitive data
• Input validation and sanitization
• Regular security testing
• Clear incident response history

Infrastructure and Deployment

What They Check

Deployment Process: How is code deployed? Is it automated? Is it repeatable?

Infrastructure as Code: Is infrastructure defined in code? Or is it manually configured?

Environment Parity: Do development, staging, and production match? Or are there environment-specific issues?

Scalability: Can the system scale? What are the bottlenecks? What happens under load?

What Raises Flags

• Manual deployment processes
• "Snowflake" servers with manual configuration
• No staging environment
• Unclear scaling limitations
• Single points of failure

What They Want to See

• Automated, repeatable deployments
• Infrastructure defined in code
• Consistent environments
• Understood scaling characteristics
• Redundancy for critical components

Team and Knowledge

What They Check

Bus Factor: How many people understand the system? What happens if key people leave?

Documentation of Knowledge: Is critical knowledge documented? Or is it in people's heads?

Handover Capability: Can knowledge be transferred? Is there a plan?

Team Assessment: How good is the team? Will they stay post-acquisition?

What Raises Flags

• Single person who understands critical systems
• No documentation of tribal knowledge
• Key people planning to leave
• Team that can't explain their own architecture
• Resistance to knowledge sharing

What They Want to See

• Multiple people who understand each system
• Documented operational knowledge
• Clear handover plans
• Strong team that plans to stay
• Culture of knowledge sharing

Data and Analytics

What They Check

Data Quality: Is data accurate and consistent? Are there integrity issues?

Data Governance: Who owns data? What are the retention policies? Are there compliance issues?

Analytics Capability: Can you measure what matters? Do you have the data to answer business questions?

Data Migration: Can data be extracted? What format? What's the migration path?

What Raises Flags

• Poor data quality or consistency
• No clear data ownership
• Compliance violations
• Inability to extract data
• No analytics or business intelligence

What They Want to See

• Clean, well-structured data
• Clear data governance
• Compliance with relevant regulations
• Accessible data for analysis
• Migration-friendly data architecture

What Kills Deals

Some issues reduce valuations. Others kill deals entirely.

Deal Killers:

• Fundamental security vulnerabilities with customer data exposure
• Undisclosed open source license violations
• Code that's actually owned by someone else
• Technical debt so severe that rewriting is cheaper than maintaining
• Key dependencies that are end-of-life with no migration path

Major Valuation Reducers:

• No tests and poor code quality (adds post-acquisition cost)
• Single points of failure in team knowledge (adds risk)
• Significant security remediation required (adds cost)
• Outdated technology requiring modernization (adds cost)
• Poor documentation requiring discovery (adds time)

Minor Concerns:

• Normal technical debt levels
• Some outdated dependencies (with upgrade path)
• Documentation gaps (that can be filled)
• Code style inconsistencies (that can be fixed)

Preparing for Due Diligence

If you're considering an exit, preparation matters.

Start early. Many issues take months to address properly. Start at least 12 months before you expect to enter due diligence.

Get an outside assessment. Internal teams are often blind to their own issues. An outside technical assessment reveals what acquirers will find.

Fix the critical issues. Not everything needs to be perfect. But deal-killers need to be addressed.

Document what you have. Acquirers fear the unknown. Documentation reduces perceived risk.

Prepare for questions. Know your architecture. Know your technical debt. Know your security posture. Be ready to explain decisions.

At Topcode, we help founders prepare for exit through technical assessment and remediation. When you work with us on exit planning, we evaluate your codebase the way acquirers will, identify issues before they become deal problems, and help you address what matters most.

Because what acquirers find in due diligence shouldn't be a surprise. It should be something you've already addressed. See also How Technical Debt Affects Your Valuation for more on the financial impact of code quality.