Monday, 9:00 a.m. An email lands in the IR team’s inbox at a global PE platform. Sender: a large pension fund. Subject line: “DDQ – due Friday.”
Attachment: an Excel file with 650 questions.
For most asset managers, eight to ten such requests arrive every week. Until recently, each one triggered the same familiar scramble: hunting for prior responses, reconciling conflicting data, confirming which language had legal approval, and coordinating last-minute inputs from investment, finance, and compliance teams.
Today, at leading platforms, the process looks very different. Questions are automatically mapped to a centralized library of approved answers, pre-populating a significant portion of the document and routing the remainder to the appropriate subject-matter experts. By Tuesday evening, a draft is ready. Wednesday and Thursday are reserved for review. By Friday morning, the DDQ is delivered—accurate, consistent, and on time.
How did large-scale private equity platforms move from constant firefighting to systems capable of processing more than 400 DDQs a year—and what does this shift signal for the future of investor relations across the industry?
Point A: An industry-wide challenge
During her tenure at a global placement agent advising private equity sponsors on capital formation, Ekaterina Dmitrieva gained a front-row view into how institutional fundraising functions across private equity firms. Working simultaneously with dozens of general partners and hundreds of institutional investors, she observed the same structural issue repeated across firms of different sizes, strategies, and geographies: content and due diligence processes were fragmented, manual, and fundamentally unscalable.
Each fundraise followed a familiar pattern. Institutional investors requested extensive DDQs, RFPs, and follow-up clarifications – often hundreds of questions per request, overlapping heavily with prior submissions. General partners, in turn, responded by recreating answers from scratch, searching across disconnected folders, prior decks, emails, and outdated versions of documents. Even well-resourced managers struggled to maintain consistency across responses, while smaller mid-market GPs were routinely overwhelmed by the operational burden.
“The inefficiency was measurable,” Ekaterina notes. “Across the industry, firms were spending 60 to 80 person-hours per DDQ. Maybe 20 of those hours involved real expertise – investment insights, performance analysis, strategic positioning. The rest was mechanical: searching for previous answers, copying content, coordinating across teams, and reconciling inconsistencies.”
She saw this as a structural challenge affecting the entire industry. Ekaterina began developing a systematic approach to institutional content management – work that eventually became the foundation for her comprehensive methodology.
Ekaterina studied economics at Moscow State University before earning her MBA at Columbia Business School. In 2024, she joined the IR team of a global PE platform. The scale of operations presented an opportunity to implement and test her systematic approach.
The platform’s scale and breadth of investor relationships made it a natural environment for applying a systematic content methodology. Managing dozens of funds and engaging with hundreds of institutional investors meant DDQs arrived continuously—from consultants, prospective limited partners, and existing clients—each requiring coordinated input from multiple internal teams.
As is typical for large, multi-strategy asset managers, these requests touched investment, operations, finance, legal, and compliance functions, drawing on information maintained across different systems and time horizons. A standard DDQ often involved several hundred questions and required careful alignment across fund vintages, strategies, and regulatory disclosures to ensure consistency and accuracy.
This complexity created a clear opportunity to introduce a more structured approach. By centralizing institutional knowledge, assigning clear ownership to approved content, and mapping recurring questions to reusable responses, the methodology transformed a process that was traditionally rebuilt for each request into one designed for scale, continuity, and repeatability—even as similar DDQs recurred with substantial overlap over time.
Methodology before technology
Ekaterina proposed a different approach: figure out how knowledge actually flows through the organization, who should own what, and how to keep things current.
They began with a focused pilot: investment strategy questions drawn from standard ILPA questionnaires. The structure took two weeks to design, followed by a month to populate the content and another month to test and refine the approach.
At the core was a structured content library. Each entry paired approved language with supporting materials, identified a named owner accountable for accuracy, and included a defined review cycle to keep information current.
Questionnaires that had previously taken two to three hours to answer were reduced to 15–20 minutes. Ready-to-use content surfaced automatically, allowing subject-matter experts to focus on review and customization rather than recreation. Consistency improved as well—investors no longer received differing descriptions of the same strategy.
The pilot proved effective.
From there, the methodology was scaled across the platform.
Scaling up
They built a full framework with major content domains covering every aspect of the fund – from firm overview to ESG, cybersecurity, and HR. Each domain was assigned a clear owner, recognizing that without accountability, institutional knowledge quickly becomes outdated.
Update cycles varied by content type. AUM and headcount got refreshed monthly. Performance data quarterly. Policies annually, unless something changed. Anything overdue shows up flagged in red.
Update cycles varied by content type. Any content approaching or exceeding its review deadline was automatically flagged for attention.
The workflow itself was formalized end to end. Incoming requests were uploaded to the system, where questions were matched against the content library, automatically populating approximately 40–60% of standard responses. The remaining questions were routed to the appropriate subject-matter experts, each seeing only their assigned items and deadlines. Completed responses then moved through two review stages: first for clarity and completeness, and then for compliance alignment.
Artificial intelligence was introduced with deliberate constraints. Performance data, fee disclosures, and legal language were always pulled verbatim from approved sources, without modification. Descriptive content could be generated from verified facts, but only with human review.
One year later
By late 2024, performance metrics reflected a clear shift. Average turnaround time for a standard DDQ fell from 14–16 days to 7–9 days, while larger requests shortened from roughly three weeks to two. Automated responses now populated approximately 45–55% of a typical questionnaire.
The impact, however, extended beyond efficiency metrics. Day-to-day work patterns changed in more fundamental ways.
IR teams reallocated their time, reducing manual, repetitive tasks by approximately 30% and redirecting that capacity toward investor engagement and relationship management.
Subject-matter experts experienced similar gains. A senior investment partner observed that a strategy section which had previously required two to three hours could now be reviewed and finalized in 30–40 minutes.
The system also shortened onboarding cycles. New hires got up to speed in three weeks instead of three months. The library captures institutional knowledge and makes it accessible.
“The system doesn’t replace expertise. It gives people time to apply judgment where it actually matters.”
Point B: A system as the new normal
By early 2024, institutional investors evaluated managers on more than performance alone. Operational maturity had become a differentiator. ILPA Principles increasingly emphasized transparency and consistency, and timely, high-quality DDQ responses emerged as a visible signal of organizational discipline and institutional readiness.
Ekaterina documented the experience in a detailed guide. It covers not just what was built, but how to replicate it: step-by-step processes, library structures, role definitions, project standards, and audit-readiness checklists.
The methodology leaves room for adaptation.
Several funds have adapted the approach to their scale. An emerging venture capital firm reported similar efficiency gains after implementing the core principles. The specifics differ – a smaller fund might handle 60 requests instead of 400 – but the fundamental challenge remains: how to scale investor communications without sacrificing quality or overwhelming the team.
Focus on the fundamentals,” she says. “Make sure every claim is accurate. Reuse what has already been proven to work. Assign clear ownership for every piece of content. Track every change. And for anything mission-critical, maintain a single source of truth.”
The question of whether other funds faced the same questions was answered almost immediately: yes.
“We initially approached it as an opportunity to refine and scale a solution within a single platform,” she adds. “It quickly became clear that the approach had relevance well beyond one firm.”
