Venture Due Diligence as a Coordinated Stack of Controls

Artificial-intelligence tools have moved from experiment to standard equipment in venture capital due diligence. According to a 2025 analysis from Affinity, 64 percent of private-capital investors reported using AI to accelerate company research, up from 55 percent in the prior year.

The same Affinity research found that 49 percent of investors rely on roughly four to six information sources for each deal they evaluate. Together, those figures indicate that VC due diligence now depends on software to aggregate data, reconcile inconsistencies, and structure questions before capital is committed.

In this environment, diligence functions as risk management for both entities and individuals. Firms define the company, subsidiaries, beneficial owners, directors, officers, and key employees as persons of interest.

They then apply a tiered review that escalates when red flags appear. Basic checks focus on identity, ownership, and market claims, while advanced reviews add sanctions, litigation, and reputational screening.

The result is an increasingly layered stack: cap-table platforms, virtual data rooms, private-market databases, AI research tools, and background-screening providers that operate as coordinated controls rather than stand-alone utilities.

From Cap Tables to Data Rooms

Ownership risk is often the first constraint in venture deals. A 2026 tech-stack survey by PortfolioIQ lists Carta as a widely used ownership and equity-management platform across funds, describing how it centralizes live cap-table data, equity plans, and valuations for portfolio companies.

Moving cap tables from ad hoc spreadsheets into systems like Carta allows investors to reconcile pro forma models with executed instruments such as SAFEs, notes, option grants, and board consents. That makes it easier to identify missing grants, inconsistent option pools, or preferential terms that could shift dilution onto new investors or employees.

Cap-table platforms also support fund-level reporting. PortfolioIQ notes that live cap-table and valuation data can feed portfolio analytics, so limited partners receive a more accurate picture of exposure across companies and financing rounds.

For individual deals, that same data helps investors confirm whether promised pro rata rights, liquidation preferences, or participation terms appear in actual documents.

Document exchange has likewise moved into structured environments. The virtual data room product from DocSend is positioned around secure document sharing with advanced permissioning, built-in non-disclosure agreements, and detailed document-tracking analytics. These features allow deal teams to restrict access to sensitive materials and monitor which counterparties review specific files.

DocSend highlights real-time visit notifications and page-level analytics, which can show who opened a data room, how long they spent on a financial model, and whether follow-on documents were accessed.

For investors, that activity record becomes part of the diligence file, supporting later questions from new limited partners or regulators about how a decision was reached and which evidence was reviewed.

Survey work summarized by David Teten on his site indicates that early-stage funds already rely on general-purpose storage tools such as Dropbox and Google Drive, as well as specialist virtual data rooms including DocSend, when sharing materials with limited partners.

That pattern extends naturally into company diligence, where control over access, version history, and audit trails is essential to managing information risk.

Layering Market and Ownership Data

Once basic ownership information is in place, many investors cross-check a startup’s narrative against external market data. The 2026 PortfolioIQ guide describes PitchBook as a private-market data platform with comprehensive coverage of millions of companies, investments, funds, and investors, along with advanced search tools for screening and analytics.

PortfolioIQ reports that PitchBook tracks roughly 4.7 million companies and 1.9 million deals, giving analysts a reference set for funding histories, valuations, and comparable transactions.

In diligence, that allows investors to compare a startup’s claimed prior rounds or cap valuations against recorded transactions, and to assess whether proposed pricing is in line with market precedents.

The same PortfolioIQ survey notes that Crunchbase maintains data on more than 4 million companies and 1.6 million funding rounds, with AI-supported features for search and trend detection. Used alongside PitchBook, it provides another perspective on investors involved, timing of rounds, and the density of competitors in a particular sector or geography.

Teten’s survey of early-stage VC funds found that many respondents regularly update their own firm records on both Crunchbase and PitchBook. That bidirectional flow of information means these databases reflect not only public disclosures but also firm-supplied corrections.

This makes them a practical reference for checking whether a startup’s investor list or deal history matches what the broader market sees.

In practice, investors use these datasets to manage what some describe as story risk: the possibility that a founder’s description of past financing, customer traction, or competitive position diverges from external evidence.

Discrepancies can then trigger follow-up requests for board consents, customer contracts, or updated capitalization data before a term sheet is finalized.

AI Enters the Workflow

AI now sits inside many of these workflows rather than on the periphery. In its 2025 due diligence article, Affinity reports that nearly two-thirds of private-capital firms use AI to accelerate company research, and frames AI as a way to surface new signals, automate research tasks, and help teams combine structured and unstructured data.

Affinity highlights its own AI products as examples of this shift. Deal Assist is described as transforming deal notes, pitch decks, and diligence data into actionable insights while reducing manual research.

Industry Insights uses AI to generate competitive landscape analysis with firmographic and funding information for target companies. These tools effectively pre-process raw material into structured inputs for investment committees.

PortfolioIQ’s 2026 survey places Deckmatch in the diligence category and describes it as an AI-powered platform for pitch-deck analysis, automated scoring, and memo generation.

The write-up emphasizes that Deckmatch benchmarks decks against large libraries of successful fundraising materials and flags gaps in narrative, financials, or market positioning, which can guide investor questions during follow-up meetings.

For organizing internal knowledge, both Affinity’s tech-stack guide and PortfolioIQ list Notion as a common workspace for diligence notes and firm playbooks.

PortfolioIQ characterizes Notion as an all-in-one workspace that combines notes, databases, wikis, tasks, and documents, allowing VC teams to build hubs for deal flow, theses, portfolio overviews, and team knowledge in one place.

When CRMs, AI summarization tools, and note hubs are used together, they support a more consistent diligence process across a fund’s pipeline.

Relationship-intelligence CRMs capture communications and introductions, AI tools extract key themes and risks from decks and calls, and workspaces like Notion keep checklists, memos, and decisions searchable. That reduces the chance that critical context about a company or founder remains in a single partner’s inbox.

Screening Entities and People

Beyond company structure and market position, many investors apply separate controls to the entities and individuals behind a deal. Kroll positions its background-screening and integrity-due-diligence services as a programmatic way to assess potential risk in customers, third parties, investments, and partners, scaling from basic checks to more intensive investigations.

Kroll’s description of its services outlines several tiers. At the lower end, screenings include sanctions and watch-list checks, global compliance databases, and adverse-media searches, along with identification of corporate registrations where available.

Public-record reviews expand this to litigation, regulatory records, professional affiliations, and confirmation of U.S. higher-education claims and professional licenses.

For higher-risk situations, Kroll offers reputational reviews and investigative due diligence that add local human intelligence, on-site public-record retrieval, and site visits.

The firm notes that these services support compliance with anti-money-laundering, know-your-customer, Foreign Corrupt Practices Act, and UK Bribery Act expectations, giving clients a documented basis for how they evaluated counterparties.

On the hiring and baseline-screening side, Checkr markets an AI-enabled background-check platform used by employers across sectors.

Its product pages describe modules for criminal background checks, employment and education verification, driving-record checks, identity verification, and international background checks, supported by APIs and integrations with applicant-tracking and HR systems.

In a venture context, tools like Checkr can be applied to founders, executives, or other key individuals when investors want to verify identity, employment history, or certain records before closing.

If initial checks surface unresolved issues, firms may escalate to a Kroll-style public-record or reputational review to obtain a deeper view of litigation history, regulatory actions, or local reputation.

Kroll’s materials emphasize a risk-based approach, in which the level of scrutiny is calibrated to the inherent risk in the opportunity.

For investors, that maps closely to the idea of tiered diligence on entities and persons of interest: routine screening for most deals, and enhanced checks for complex jurisdictions, politically exposed persons, unusual related-party structures, or sectors with heightened corruption or sanctions exposure.

Voices From the Market

While product sites and vendor surveys show which tools are available, David Teten’s survey of early-stage VC funds adds context on how investors are actually using them. The report, published on Teten.com, summarizes responses from 137 managers with fund sizes of 200 million dollars or less about their technology stacks and budgets.

In that survey, Lisa Edgar of Top Tier Capital Partners is quoted as saying that it is not surprising that venture capitalists are using software to manage their business. She notes that the landscape has become more competitive and crowded to the point where investors who do not use software tools are at a disadvantage.

She also notes that proprietary systems require ongoing investment in data quality for any later use of machine learning or AI to be meaningful.

Clint Korver of Ulu Ventures is quoted in the same report comparing the technology transformation in venture capital to earlier changes in public equity investing.

He recalls that public markets shifted from mutual funds built around individual stock-picking toward hedge funds and quantitative approaches, and he expects a similar shift in venture over the following decade or two.

Teten’s write-up also points out that even smaller early-stage funds reported spending significant amounts on technology by 2017 and that a majority planned to increase their tech budgets.

That willingness to invest in CRMs, data providers, and virtual data rooms provides the foundation for the newer AI layers described by Affinity and PortfolioIQ, because automation depends on having usable data in the first place.

What Changes Next

Taken together, these sources suggest that VC due diligence is shifting from a set of informal checks to a structured risk-management system that relies heavily on software.

Cap-table platforms clarify who owns what, virtual data rooms create auditable evidence trails, private-market databases challenge or confirm company narratives, and AI tools summarize large volumes of material into targeted questions.

Affinity’s due diligence article describes how AI helps investors combine structured metrics with unstructured signals and argues that this leads to fewer but higher-quality deals.

PortfolioIQ’s mapping of hundreds of tools shows that AI is now present in sourcing, screening, diligence, relationship intelligence, and reporting, not just in isolated research tasks.

As adoption deepens, the differentiator is less whether a firm uses these tools and more how systematically it applies them to entities and individuals across the portfolio.

Firms that maintain clear risk registers, tie findings to mitigations, and keep data and documents in systems that can be queried later are likely to be better positioned when limited partners, co-investors, or regulators ask how a particular decision was made.

Sources

• Elfman, L. "How and why venture capital due diligence is evolving." Affinity, 2025.
• Affinity. "Build the ultimate venture capital tech stack in 2025." Affinity, 2025.
• PortfolioIQ. "The Definitive VC Tech Stack: 500+ Tools Funds are Actually Using in 2026." PortfolioIQ, 2026.
• PitchBook. "Private Market Data, Research & Tools." PitchBook, N/A.
• DocSend. "Virtual Data Rooms for Deals, Fundraising, and Compliance." DocSend, 2026.
• Kroll. "Screening and Due Diligence | Background Checks and Screening." Kroll, 2026.
• Teten, D.; von dem Knesebeck, P. "What’s your VC tech stack? Results from a survey of early-stage VC funds." Teten.com, 2017.
• Checkr. "Employee Background Screening for Companies." Checkr, N/A.