The short answer: Technical due diligence exists to answer one question — is the technology worth what the deal assumes it is worth? For software-heavy and AI targets, that answer routinely moves the price. Technical diligence has become one of the most consequential workstreams in a modern deal, and it is standard practice for software transactions of meaningful size. This checklist covers what a thorough review should examine.

Why technical due diligence matters more than it used to

Software and AI companies are being valued on the strength of their technology and their team. When that technology is the asset, an independent look under the hood is not optional — it is where deals are re-priced, and occasionally where they are walked away from. Findings frequently swing negotiations on software-heavy deals, which is precisely why acquirers and investors pay for an independent read rather than trusting the data room.

Use the checklist below as a structure for that read.

1. Architecture and scalability

  • Does the architecture match the company’s stage and growth plans, or is it over-built or dangerously under-built?
  • Where does the system break at the next 10x of load or data? Are those limits known to the team?
  • How much of the design is deliberate versus accreted? Accidental architecture is a future cost.
  • Are there single points of failure, or dependencies on one irreplaceable component or person?

2. Code quality and technical debt

  • Is the codebase maintainable — consistent, tested, and documented enough for a new engineer to be productive?
  • What is the real technical-debt load, and how much of the roadmap will it silently consume?
  • Is there automated testing and CI, or does every release depend on heroics and hope?

3. Security and data

  • How is sensitive data handled, stored, and access-controlled? Any history of breaches or exposure?
  • Are there obvious vulnerabilities, unpatched dependencies, or weak authentication and authorisation?
  • Is the company’s data-protection posture consistent with the regulations it operates under?

4. Intellectual property and open-source risk

  • Does the company actually own its core IP? Were contractors and founders under proper assignment agreements?
  • What open-source licences are in use, and do any of them (for example, copyleft licences) create obligations that threaten the commercial model?
  • Are there third-party dependencies whose licensing or viability puts the product at risk?

5. AI and machine-learning claims

This is where diligence most often changes a valuation, and where a data room is least trustworthy without verification.

  • What does the AI actually do, versus what the deck says it does? Is it genuinely a model, or a thin wrapper over someone else’s?
  • How defensible is it — proprietary data, real evaluation, or a prompt anyone could replicate in a weekend?
  • What are the data rights underneath the model? Was training data acquired legally, and can the company keep using it?
  • Are cost, latency, and reliability at production scale understood — or has the system only ever run at demo scale?

6. Team, process, and key-person risk

  • Is engineering leadership credible, and how much knowledge lives in one or two heads?
  • What is the delivery process — can the team ship reliably, or does the roadmap depend on optimism?
  • What is the attrition risk around the people who actually understand the system?

7. The output that makes it useful

A diligence report is only worth having if it is decision-useful. It should deliver:

  • Findings, ranked by severity — not an undifferentiated list of observations.
  • Business impact for each finding, in language an investment committee understands.
  • Estimated remediation cost and time, so issues can be priced into the deal.
  • A clear verdict on whether the technology supports the thesis.

Key takeaways

  • Technical diligence is now one of the most consequential workstreams in a software or AI deal, and it routinely affects price.
  • Cover six areas: architecture, code quality, security, IP, AI claims, and team — then translate findings into severity, impact, and cost.
  • AI claims deserve special scrutiny; the gap between narrative and reality is widest there.
  • Independence is the value — an outside firm has no incentive to see the deal close.

This is exactly the review we run for investors and acquirers, on your deal timeline. See our technical due diligence service or book a scoping call.