Technology due diligence has a credibility problem. Firms commission it, receive a document full of findings, and then struggle to translate those findings into anything actionable at the deal table. The report describes the technical debt. It does not say what remediating it costs. It identifies key person dependency. It does not say how to address it structurally. It flags security gaps. It does not say whether they are a negotiating point or a deal-breaker.

This is not due diligence. It is a technical audit dressed up as one.

PE firms — particularly those moving faster or at higher deal values — need something more specific from their TDD providers. Here is what that looks like.

Direct access to the codebase, not management presentations

The single most reliable signal in any TDD is what the codebase shows when examined directly, not what the management team reports about it. Key person dependency, test coverage, dependency health, architecture coherence — none of these can be verified from a PowerPoint. All of them can be assessed in hours with the right tooling and direct repository access.

A TDD that does not include direct codebase inspection has a material blind spot. Insist on it as a baseline requirement, not an optional scope item. The target company may resist; the more important it becomes to push for it.

Findings expressed as remediation costs, not just risk ratings

A finding rated “High” tells you something went wrong. A finding that says “remediating this will require six months of engineering time and approximately £150k before the platform can support the growth thesis” tells you something you can act on.

The purpose of technical due diligence in an M&A context is to inform deal structure: price, escrow, earn-outs, post-close conditions, contractual representations. None of those levers can be calibrated from a risk matrix. They require quantified estimates of what it will cost to resolve the issues found — in time, in money, and in the impact on the business’s operating capacity during remediation.

Demand that your TDD provider translates findings into financial and timeline terms. If they cannot or will not, they are not providing deal intelligence. They are providing a findings list.

Scalability assessed against the investment thesis, not current state

One of the most common and costly TDD failures is assessing a platform’s scalability under its current load rather than under the load implied by the investment thesis. A platform that handles 50,000 users today with acceptable performance may not handle 500,000. A system that processes 1,000 transactions per day may not process 50,000. These are not hypothetical concerns — they are the scenarios the acquisition is predicated on.

Your TDD provider should receive the investment thesis, understand the projected growth trajectory, and assess whether the platform’s architecture can support it. If it cannot, the cost and timeline of re-architecture should be part of the findings. Assessing scalability risks in early-stage companies requires a different framing than a standard audit — one that starts from growth assumptions rather than current performance metrics.

An independent view of team capability, not just headcount

Engineering team assessments in TDD reports tend toward the superficial: role count, tenure, geographic distribution. These describe a team. They do not assess whether the team can execute the post-acquisition roadmap.

What PE firms need is an independent view of whether the team has the skills and depth to sustain and extend the platform after close — particularly once the founding engineers, who may leave, are no longer involved. Key person dependency is a deal structuring question. Retention risk is a negotiating point. Neither can be managed without an honest assessment, and that assessment requires practitioners who can evaluate engineering capability directly rather than through an HR lens.

IP ownership and AI readiness treated as standard scope

IP ownership verification — assignment agreements for founders, employees, and contractors; open source licence compliance; AI-generated code provenance — should not be an optional add-on. It is a standard component of any acquisition involving a software-dependent asset, and the cost of missing it post-close is disproportionate to the cost of including it in scope.

The same applies to AI readiness. For any platform with AI-dependent features or an AI-forward investment thesis, the TDD should assess whether AI capability is genuinely defensible — built on proprietary data, fine-tuning, or architecture — or whether it is a thin integration layer that could be replicated cheaply. It should also assess compliance with applicable regulation, including the EU AI Act for platforms with European operations or users.

The right question to ask any TDD provider

Before commissioning a technology due diligence, ask the provider one question: In your last five engagements, how did your findings affect the final deal structure?

A provider who can answer that question with specific examples — price adjustments, escrow provisions, post-close conditions tied to technical milestones — is doing deal intelligence. A provider who cannot is doing technical audits.

The distinction matters. In a competitive process with a compressed timeline, a findings list adds marginal value. A structured technical view that informs the price you pay and the conditions you impose is what PE firms should be demanding — and what the best TDD providers are able to deliver.

At VeryDiligent, we work with PE firms and strategic acquirers at the intersection of technical assessment and deal structure. If you are approaching a transaction and want to understand what a proportionate engagement looks like, we would be glad to discuss it.

Related reading: Technology Due Diligence vs. Technical Audit: What Buyers Get Wrong | How to Choose the Right Technology Due Diligence Partner | Technology Due Diligence: Operational and Strategic Value Creation