The Technology Due Diligence Checklist for PE Firms

In a technology-driven deal, the engineering organization is a large part of what you're buying. Yet diligence often stops at a surface read of the codebase and a few management interviews. The result is post-close surprises — re-platforming costs, key engineers who leave, security debt, or an "AI-enabled" product whose models don't hold up. This checklist covers the seven areas that determine whether the technology supports the investment thesis.

1. Architecture & scalability

• Is the architecture able to support the growth assumed in the model, or will it need re-platforming within the hold period?
• What are the single points of failure and hard scaling limits?
• How much of the system is bespoke versus standard, supportable technology?
• What is the realistic cost and timeline of the next major architectural change?

2. Code quality & technical debt

• What does the codebase look like on objective measures — test coverage, complexity, change-failure rate, and the age of critical components?
• How much technical debt is accumulated, and how much of it is blocking the roadmap?
• Is delivery predictable? Pull-request cycle times, deploy frequency, and sprint velocity tell you more than a demo does.

3. Security posture

• Where does the target stand against a recognized framework — SOC 2, NIST CSF, ISO 27001, or CIS?
• Any history of breaches or unresolved critical vulnerabilities?
• How are identity, access, secrets, and data protection handled?
• What remediation is required, at what cost, and on what timeline?

4. Engineering team & key-person risk

• Who holds the critical knowledge, and what happens if they leave? Concentrated knowledge is one of the most common — and most expensive — diligence findings.
• Is the team structured to scale, or organized around a few heroes?
• What are retention risks through and after the transaction?
• What hiring is required to deliver the plan, and is that talent available?

5. Data assets & AI readiness

AI has become a core diligence dimension. Buyers increasingly assess not just risk but upside — whether AI can credibly be part of the value-creation plan. Evaluate:

• The quality, ownership, and defensibility of the company's data assets.
• For "AI-enabled" targets: model quality, sustainability, and whether claimed capabilities hold up under scrutiny.
• AI talent depth and retention risk.
• Whether AI use cases are tied to a measurable line of sight to EBITDA, or are aspirational.
• Regulatory and governance exposure around AI and data.

6. Integration & separation cost

• For a bolt-on: what will it actually cost and take to integrate systems, data, and teams?
• For a carve-out: what shared services and dependencies must be separated, and how long will that take?
• What licensing, cloud, and vendor commitments transfer with the deal?

7. Cloud cost & operating efficiency

• Is cloud spend efficient, or is there material waste affecting margins?
• Are there contractual lock-ins or commitments that constrain flexibility?
• What's the run-rate trajectory of infrastructure cost as the business scales?

Turning findings into deal impact

The output of technology diligence shouldn't be a list of observations — it should be a risk-rated view tied to the thesis: what threatens the plan, what it costs to fix, and what upside is real. The strongest diligence pairs automated metrics (which are fast, objective, and hard to argue with) with senior judgment about what those numbers mean for this specific deal.