Due Diligence

How Allocators Can Use AI-Powered Prefilling to Accelerate DDQs

How allocators can use AI-powered prefilling to reduce back-and-forth, improve consistency, and streamline the due diligence process.

Dasseti
Jul 2, 2025
Due Diligence

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When allocators think about DDQ efficiency, most focus on response deadlines and question volume. But one of the most underutilized levers for speed and clarity is prefilling – especially when it’s used intentionally before a DDQ goes out.

This blog explores how allocators can use AI-powered prefilling not only to reduce time-to-response, but also to support better data consistency, reduce back-and-forth, and generate a more scalable due diligence process over time.

How Allocators Use Prefilling and AI to Streamline DDQs

In most platforms, prefilling is only positioned as a tool for managers to reuse past answers. But in Dasseti COLLECT, allocators can also use it proactively to accelerate their own workflows, especially when monitoring known managers or requesting information already held from previous cycles.

With Dasseti COLLECT, allocators can prefill DDQs by:

  • Reusing validated data already collected in the system
  • Pulling from previous DDQ cycles, broken down by fund, firm, or question
  • Scraping data from uploaded manager documents

Instead of sending a blank questionnaire and hoping for consistency, allocators start from a baseline and give managers the opportunity to confirm or update.

The Benefits of AI-Powered Prefilling in Due Diligence Workflows

Yes, prefilling accelerates response timelines – but that’s only part of the benefit. Done well, it also:

  • Improves data quality: When managers can start from a previous answer, they’re less likely to misinterpret the question or omit key context.
  • Reduces friction: A prefilled DDQ shows managers that you’ve already done your homework, which strengthens the allocator-manager relationship.
  • Creates a feedback loop: Over time, you’ll be able to refine your templates based on which fields are frequently updated, clarified, or corrected.

This is how smart DDQ workflows scale – not just by speeding up responses, but by improving the structure of what comes back.

How to Use AI Prefilling Without Sacrificing Accuracy

The key to successful prefilling is to support – not shortcut – the diligence process. Allocators should build in optional comment fields, encourage updates, and never assume that past data is still current without review.

Here’s how Dasseti COLLECT enables accurate prefilling at scale:

  • Single-question precision: Rather than dropping full DDQs into a large language model, each question is addressed independently with relevant data attached – reducing hallucination risk and improving traceability.
  • Allocator-side document scraping: Teams can upload previously received documents (e.g. DDQ responses, factsheets, regulatory filings) to auto-populate certain questions.

It’s a smarter way to accelerate workflows without cutting corners – especially when consistency and auditability matter.

Structured Data Is the Foundation for Scalable AI

AI can help allocators move faster – but only if the data behind it is structured and reusable. When responses are pulled from PDFs or past documents without being stored in a visible, queryable format, they’re difficult to trace, compare, or build on later.

Dasseti takes a data-first approach. Every response is tied to a fund or firm profile, stored in a structured format, and ready for future use – whether that’s your second monitoring cycle or a long-term trend analysis.

This foundation is what enables scalable AI-powered due diligence. You’re not just speeding up workflows – you’re building institutional knowledge that gets stronger with every DDQ.

Real-World Example of Allocator-Side AI Prefilling

Let’s say you’re preparing a quarterly monitoring DDQ for 30 managers. You already know most of them are still regulated by the same authority, operating with the same fund structure, and using the same key service providers.

Instead of asking them to retype the same answers, you prefill the known fields – say 40% of the DDQ – and leave space for confirmation or updates. Managers save time. You get better data.

Used correctly, prefilling isn’t just about convenience – it’s about building a faster, more intelligent due diligence workflow that scales. The key is pairing structure with flexibility, and automation with allocator control.

Want more tips?

Download our free guide 'Designing Smarter DDQs', put together specifically for allocators looking to reduce admin, improve response quality, and make better use of data and AI.

DOWNLOAD THE GUIDE

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