Perspectives

When investors evaluate a technology company – particularly in AI and emerging tech sectors – conventional due diligence, looking at financial statements and market positioning is only part of the story. Technical due diligence (TDD) provides the critical assessment of the underlying technical foundation: the code, infrastructure, data, and technical capabilities that determine whether a company can deliver on its promises. 

Technical due diligence is a systematic evaluation of a company’s technology assets, architecture, and engineering practices. Unlike financial or legal DD, technical due diligence examines the actual systems, code quality, data infrastructure, and technical team capabilities that underpin the business. It examines the core technical infrastructure and seeks to evaluate the technical assets and technical substance behind the business.

For AI companies, this process takes on additional dimensions. Beyond traditional software evaluation, TDD must assess model performance, data quality and provenance, algorithmic bias, scalability of ML infrastructure, and compliance with emerging AI regulations.  

TDD looks at and technical capabilities that determine whether a company can deliver on its promises. 

A comprehensive technical due diligence engagement for AI companies typically covers several key areas:

Technology Stack Assessment: Evaluating the architecture, infrastructure choices, and scalability potential. Are systems built to handle projected growth? Are technology choices appropriate and sustainable?

Architecture and Model Infrastructure: Evaluating the overall system architecture, including how models are integrated, deployed, and served. Are inference pipelines scalable? Is the architecture designed to handle projected growth in usage and model complexity?

Code and Model Quality: Examining codebase structure, documentation, testing practices, and technical debt – alongside model versioning, reproducibility, and the rigour of ML experimentation practices. Well-maintained code and disciplined MLOps indicate engineering maturity and reduce future risk.

Data Strategy and Governance: Reviewing data pipelines, quality controls, storage solutions, and provenance documentation. Critical questions include data rights, licensing for training data, compliance with privacy regulations, and whether data assets are defensible and appropriately documented.

Security, Compliance, and AI Regulation: Identifying vulnerabilities, assessing cybersecurity practices, and ensuring compliance with relevant regulations. For AI companies, this now includes emerging AI-specific requirements such as the EU AI Act, transparency obligations, and sector-specific AI guidance.

Team and Capability Assessment: Evaluating whether the technical team has the specialised skills to execute the AI roadmap. This includes ML engineering depth, research capability, and key person dependencies – particularly around individuals who understand both the models and the domain.

Model Performance and Responsible AI: Assessing model accuracy, robustness, and fitness for purpose. This includes evaluating bias testing methodologies, explainability capabilities, monitoring for drift, and the maturity of responsible AI practices.

Beyond these structured assessment areas, AI TDD demands particular attention to elements that distinguish AI ventures from traditional software companies. Model intellectual property requires careful examination: understanding whether competitive advantage stems from proprietary architectures, unique training data, fine-tuning approaches, or simply prompt engineering around foundation models yields very different risk profiles. The dependency landscape matters – companies building on third-party APIs face different scaling economics and strategic risks than those with in-house model capabilities.

TDD Consultants must also evaluate the company’s ability to adapt as the underlying technology evolves rapidly; the AI landscape of twelve months hence may render current technical approaches obsolete, making the team’s learning velocity and architectural flexibility as important as current capabilities. 

One element that needs to be assessed during technical due diligence is technical debt. This term was coined by Ward Cunningham (1992) who described technical debt as a metaphor for understanding the long-term costs of software development decisions. Just as financial debt allows you to do something now but requires interest payments until repaid, technical debt represents architectural decisions, shortcuts or suboptimal code that enables faster initial development but creates ongoing maintenance costs. 

Technical debt manifests in various forms: poorly structured code, inadequate documentation, outdated dependencies, lack of test coverage, or architectural decisions that seemed reasonable at the time but don’t scale. The challenge isn’t that technical debt exists – some level of debt is inevitable and even strategic – but rather whether it’s identified, measured, and managed effectively. 

During TDD, the TDD Consultants will identify technical debt through code reviews, architecture analysis, and discussions with development teams. They look for warning signs: lengthy build times, frequent production incidents, difficulty onboarding new developers, or features taking progressively longer to implement. Quantifying technical debt involves examining metrics like code complexity, duplication rates, test coverage, and the ratio of time spent on new features versus maintenance work. There are some automated tools that can help measure code quality and estimate remediation effort, but the human judgment of experienced TDD consultants remains essential for understanding the business impact of accumulated debt. 

The distinction matters because not all technical debt is created equal. Deliberate, prudent debt – taking a shortcut to validate a market hypothesis – differs from inadvertent debt resulting from poor engineering practices. Understanding which type predominates, and whether the organization has processes to pay down debt systematically, provides important insight into the sustainability and scalability of the technology platform. 

Technical due diligence protects against expensive surprises. A company might show strong revenue growth, but underlying technical debt, security vulnerabilities, or unsustainable infrastructure costs can destroy value post-acquisition. For AI ventures, poorly documented data provenance or biased models can create existential legal and reputational risks. 

For startups themselves, undergoing TDD – even without an immediate transaction –provides valuable insights. It identifies technical risks before they become crises, validates technology decisions, and demonstrates maturity to future investors or partners. 

As AI becomes increasingly embedded in professional services, from real estate to finance, technical due diligence has evolved from a specialised investment tool to an essential governance practice. Whether you’re raising capital, considering acquisition, or simply ensuring your technology foundation is sound, understanding and embracing the TDD process is no longer optional – it’s fundamental to responsible technology leadership. 

Technical Due Diligence

Andriole, S. J. 2008. Technology Due Diligence: Best Practices for Chief Information Officers, Venture Capitalists, and Technology Vendors. IGI Global. 

NIST. 2023. Artificial Intelligence Risk Management Framewwork. Nastional Institute for Standards and Tecnology. January 2023. 

Thought Source Consulting. 2024. AI and ML in Technology Due Diligence. Available at: https://www.thoughtsourceconsulting.com/articles/ai-and-ml-in-technology-due-diligence/ 

Technical Debt

Agile Alliance.2016. Introduction to the Technical Debt Concept. Available at: https://agilealliance.org/introduction-to-the-technical-debt-concept/  

Alves, N. S. R., et al. (2016). Identification and Management of Technical Debt: A Systematic Mapping Study. Information and Software Technology, 70, 100-121. 

Cunningham, W. 1992. The WyCash Portfolio Management System. OOPSLA ’92 Experience Report. The original articulation of the technical debt metaphor. 

Fowler, M.  2019. Technical Debt. Available at: https://martinfowler.com/bliki/TechnicalDebt.html 

McConnell, S. 2008. Managing Technical Debt. Available at: Construx Software. A framework for categorizing technical debt as deliberate/inadvertent and reckless/prudent.