How AI Strengthens Technical Due Diligence in Private Equity
By Jean-Gabriel Prince
Short answer: AI is useful in technical due diligence when it is pointed at the questions the deal team needs answered. How healthy is the codebase? Where is the security and licensing exposure? How dependent is the product on a few engineers? Do the technology claims in the CIM hold up? Used this way, AI lets a small team read far more of the target's material inside a short deal timeline. Final judgment still belongs to experienced reviewers.
What Technical Due Diligence Has to Answer
Technical diligence exists to inform the investment thesis. The committee wants to know three things. Can the technology support the value creation plan? What will it cost to fix what is broken? What could surface after close? Those questions turn into a short list of workstreams:
- Code quality and architecture
- Security posture and open-source license exposure
- Infrastructure, scalability and cloud cost
- Data assets and how they are governed
- Team concentration and key-person risk
- Product claims compared with what is actually built
Where AI Adds Real Leverage
Codebase Review at Scale
Human reviewers usually work from samples. With repository access, language models paired with static analysis tools can read the whole codebase. They can map the architecture, flag dead or duplicated code and summarize how each service works. The reviewer can then go straight to the parts that matter.
Data Room Triage
Data rooms are large and often poorly organized. AI can sort documents and pull commitments out of vendor and customer contracts. It can also find inconsistencies between architecture diagrams, incident reports and management presentations. Every extracted claim should link back to its source page so a reviewer can check it.
Repository History and Team Risk
Commit history shows who actually built and maintains each component. Analyzing it reveals key-person concentration, stalled modules and gaps between the org chart and the working engineering team. That feeds directly into retention planning and the first 100 days.
Testing the Target's AI Claims
Many targets now market themselves as AI companies. Diligence should establish what is proprietary and what is a thin layer over a third-party model. It should also cover what rights the company holds to its training data and whether reported model performance can be reproduced. Someone who has built production models can usually tell the difference quickly. AI tooling helps by tracing where model calls happen in the code and what data they touch.
Where AI Falls Short
- It cannot judge fit with the thesis. Whether a monolithic architecture is a problem depends on the growth plan.
- It produces confident errors. A person must verify every material finding before it reaches the IC memo.
- It only sees what it is given. Restricted access, redacted code or a curated data room limit any tool.
- Management interviews still matter. How a CTO handles hard questions about incidents and technical debt reveals things no repository will.
How to Set It Up on a Live Deal
- Start from the IC questions. Write down the five or six findings that would change price, structure or the decision. Scope the analysis to those.
- Agree on data handling with the seller. Code and data room contents are confidential. Run models in an environment that meets the NDA terms, with no retention of deal material and no training on it.
- Keep a source trail. Each finding should cite the file, commit or document it came from.
- Put a senior reviewer on the output. AI speeds up the reading. A person who has run engineering or shipped production systems signs off on the conclusions.
- Translate findings into money and time. The committee prices remediation cost, timeline and risk to the plan.
What This Means for the Deal Team
From the investment committee seat, the test of technical diligence is simple: did it change the decision or the price? AI makes it practical to cover more ground in the same window. That means fewer surprises after close and a more specific value creation plan on day one. The firms that benefit run it as a disciplined workflow, with clear questions, controlled data and accountable reviewers.
If you are planning how to use AI on your next technical diligence, book a discovery call.