Why Full Standardization Fails

A single fixed DDQ, applied identically to every deal, tends to fail in one of two directions.

Either it's built broad enough to cover every possible sector, which means most deals wade through questions that don't apply to them.

Or it's built narrow around the fund's most common deal type, which means the exceptions, the deep-tech deal, the AI-native product, the cross-border cap table get under-asked exactly where more scrutiny was needed.

Why Full Customization Fails Too

The opposite approach, a fresh questionnaire built for every deal, solves the relevance problem but breaks comparability. If deal five and deal twelve were assessed on different questions, there's no consistent basis to compare them, no reusable template for onboarding a new analyst, and no clean way to demonstrate to an LP that the fund runs a repeatable process rather than an ad hoc one each time.

A Base-Plus-Modules Structure

The structure that tends to hold up: a fixed core DDQ covering financial, legal, team, and market questions that apply to every deal regardless of sector, plus specialist modules that switch on based on deal characteristics rather than being rebuilt from scratch.

• Core module: always included: financials, cap table, legal structure, IP ownership, team background

• ESG/SFDR module: included when the fund needs sustainability data for its own reporting, or wants a structured read on the founder's approach to these topics. See ESG Due Diligence for European VC.

• Dual-use module: included for deep tech, defence-adjacent, or otherwise sensitive sectors. See Dual-Use Due Diligence for Deep Tech VCs.

• AI Act module: included where the company builds or deploys AI systems. See Responsible AI Due Diligence for VCs.

• KYC/KYT module: included for every deal at a baseline level, with additional depth for cross-border ownership structures. See KYC and KYT for VC Due Diligence.

This way, every deal produces a comparable core, and the modules that get switched on reflect genuine deal characteristics rather than reflecting whichever analyst happened to build the questionnaire that week.

Deciding Which Modules Apply

The decision to switch a module on should happen at screening, not partway through diligence — see the sourcing and screening stage in The European VC Due Diligence Workflow. A simple rule of thumb: if a sector or deal characteristic is flagged during screening as potentially triggering ESG, dual-use, AI, or KYC/KYT considerations, the relevant module goes into the DDQ from the start rather than being added later once someone notices the gap.

Keeping the Template Current

A DDQ template isn't a document you build once. Regulatory categories shift, the EU AI Act's own applicability timeline has moved more than once, and a template that isn't reviewed periodically drifts out of date quietly, often without anyone noticing until a gap shows up mid-deal.

Building periodic review into the process, not just periodic use, is what keeps the base-plus-modules structure actually reliable rather than a snapshot of what was relevant a year ago.

Where This Feeds the Report

A standardized DDQ structure pays off again downstream: because the core module is consistent across every deal, the resulting DD report can follow a consistent shape too, even as the specialist sections that appear change deal to deal. That consistency is what makes the report readable at speed by an investment committee that's seen the same shape before.