AI in Private Markets: How Faster Research Is Raising the Underwriting Standard
Artificial intelligence is becoming integral to private-market investing, with specialized technology firms changing how investors conduct research, due diligence, credit analysis, and portfolio monitoring.
A 2026 Datasite and FT Longitude survey found that 50% of dealmakers regularly use or have fully embedded AI in due diligence.¹ McKinsey reports productivity gains of 30% to 40% in analyst-intensive tasks at some private equity firms, although adoption remains uneven.²
As AI reduces the time and cost of analyzing private-company information, investment teams can assess more opportunities and identify risks earlier. This is also raising underwriting expectations, placing greater importance on reliable financial disclosures, verifiable evidence, and well-supported investment assumptions.
For private credit investors, and capital providers, these developments have direct implications for risk assessment and capital allocation.
The Rise of AI-Native Private Market Intelligence
Private-market investment research has traditionally relied on financial databases, advisors, and labor-intensive diligence. Unlike public markets, private-company information is fragmented across confidential data rooms, financial statements, and transaction documents.
AI-native platforms such as ToltIQ, Hebbia, Rogo, and 73 Strings are addressing this fragmentation through specialized research, document analysis, due diligence, and valuation tools.
Institutional adoption is accelerating. In March 2026, H.I.G. Capital selected ToltIQ for firm-wide AI-powered due diligence.³ In June, PwC announced a strategic relationship with ToltIQ to incorporate AI into deal execution and advisory workflows.⁴ Established providers are also expanding their capabilities. In October, PitchBook introduced AI-powered research tools and integrations connecting its proprietary data with investors' internal systems.⁵
These developments signal a broader shift toward AI embedded in investment workflows. For investors, competitive advantage will increasingly depend on access to proprietary information, analytical reliability, and integration with institutional decision-making processes.
Mapping the Emerging AI Private Markets Ecosystem
The competitive landscape is developing across several distinct business models.
AI-native platforms are built around AI-powered research, due diligence, financial analysis, and investment workflows. Firms such as ToltIQ, Hebbia, Rogo, and 73 Strings fall within this segment.
AI-integrated platforms are established financial information and private-market technology providers incorporating AI into their existing products. PitchBook, AlphaSense, 9fin, Datasite, and BlackRock's Preqin and eFront platforms illustrate different approaches within this category.
AI-enabled professional services combine proprietary technology or external AI platforms with advisory expertise. PwC's relationship with ToltIQ demonstrates how technology developers and established transaction advisors are forming strategic partnerships.
Adjacent infrastructure providers support private-market transactions, ownership records, liquidity, and portfolio administration. Platforms such as Carta, Hiive, and EquityZen form part of the broader ecosystem, although AI is not necessarily central to their primary services.
Competition is increasingly crossing these categories.
In October 2026, Datasite announced a partnership with Hebbia that allows authorized users to analyze live data-room documents alongside internal research, public filings, and market information, while maintaining access controls and source citations.⁶
The development illustrates a wider direction for the industry: specialized AI capabilities are being connected to established financial databases and transaction systems.
As these integrations expand, the commercial value of proprietary datasets, client relationships, and access to investment workflows becomes more significant. A provider's ability to deliver reliable analysis within an existing institutional process could matter as much as the underlying AI model.
Faster Due Diligence Is Changing the Economics of Investment Screening
AI is reducing the time and resources required for preliminary investment research and due diligence, allowing firms to screen more opportunities before committing significant analyst resources.
For example, a lender evaluating a recurring-revenue technology business can use AI-assisted tools to review historical revenue, customer concentration, cash burn, existing debt, and management forecasts. These tools can identify inconsistencies, locate contractual provisions, and compare financial assumptions across documents, allowing analysts to focus on material risks.
The broader implication is a higher standard of evidence. As financial information becomes easier to examine, unsupported growth assumptions, reporting discrepancies, and missing documentation become more visible earlier in the process.
Borrowers and sellers should therefore prioritize reconciled financial statements, documented revenue adjustments, and credible forecasts supported by operating assumptions. Lenders and investors, meanwhile, can investigate a wider range of risks before advancing transactions.
AI may still have limitations. Its findings depend on the completeness and accuracy of underlying information, and human judgment remains essential to assessing commercial assumptions and financial risk.
Implications for Private Credit Underwriting
Private credit underwriting requires more than an assessment of enterprise value or historical growth.
Lenders need to establish the borrower's capacity to service its obligations under realistic operating conditions. That involves examining cash generation, existing liabilities, contractual commitments, liquidity, collateral, and downside scenarios.
AI-powered platforms are developing capabilities that support these processes.
In August 2026, 9fin introduced Private 9fin, expanding its credit intelligence platform to incorporate confidential private-company financials, documents, earnings calls, and proprietary analysis.⁷
For credit investors, such capabilities have practical applications in reviewing loan agreements, identifying covenant provisions, comparing borrower reporting, and monitoring developments across portfolios.
Another interesting opportunity lies in applying this research to a more rigorous assessment of repayment risk.
A lender evaluating a growth-stage borrower, for example, could use AI-assisted document review to identify material customer contracts, locate debt restrictions, and compare reported financial performance with supporting schedules.
The credit decision should still requires judgment about the durability of revenue, operating cash needs, financing structure, and the borrower's ability to withstand weaker performance.
For borrowers, the implication remains that financing discussions increasingly benefit from detailed evidence of debt capacity, liquidity needs, and the assumptions supporting future repayment.
The Next Competitive Advantage: Proprietary Data and Verifiable Investment Judgment
As AI capabilities become more accessible, private-market technology providers face a central commercial question: which capabilities will remain differentiated?
General-purpose language models already support document summarization, financial research, and analytical tasks. Specialized providers need to demonstrate value through proprietary information, institutional integrations, repeatable workflows, and reliable outputs.
BlackRock's February 2026 integration of Preqin data and technology into its eFront platform illustrates the strategic importance of combining investment information with established private-market systems. The integration includes AI-assisted manager research and other data-driven investment analytics.⁹
This direction has implications for both technology providers and investment firms.
For providers, access to specialized datasets and established institutional clients could become increasingly important sources of competitive advantage.
For investment firms, the central challenge is establishing how AI-assisted analysis fits within existing risk management and decision-making standards.
That requires attention to source verification, document version control, data confidentiality, access permissions, and accountability for investment recommendations.
These concerns are already visible in industry research. The 2026 Datasite and FT Longitude survey found that 71% of respondents ranked accuracy among the most important attributes of AI tools, while 58% reported using human review or validation to build trust in AI-generated outputs.¹
Investment firms evaluating these technologies should therefore measure performance against the quality of the work produced.
Useful indicators include the time required to identify material diligence exceptions, the proportion of findings supported by primary documentation, the accuracy of extracted financial information, and the extent to which research improves investment committee preparation.
The longer-term commercial opportunity rests with providers that combine specialized information, reliable analytical systems, and workflows designed around actual investment decisions.
What AI Means for the Future of Private-Market Capital Allocation
AI is becoming embedded in private-market investment and transaction workflows, allowing firms to assess more opportunities, identify risks earlier, and conduct deeper diligence with fewer manual resources.
For borrowers and sellers, reliable financial reporting, credible forecasts, and transparent documentation will become increasingly important in securing capital and supporting valuations. Investors and lenders, in turn, can strengthen underwriting while maintaining discipline around repayment capacity and downside risk.
From an Axis Group Ventures perspective, AI's long-term value lies in its ability to support better-informed financing decisions, more disciplined capital allocation, and stronger underwriting across private markets.
About Axis Group Ventures
Axis Group Ventures is a boutique investment banking and strategic advisory firm focused on global debt placement and private-market secondaries for venture- and private equity-backed companies. We work with founders, C-suite executives, and investors to develop and execute customized private-market capital solutions.
For more information, visit www.axisgroupventures.com.
Sources
Datasite, Where Should AI Start on a Deal?, August 19, 2026. Cites findings from the 2026 Datasite and FT Longitude survey of 1,000 dealmakers.
McKinsey & Company, Global Private Equity Report 2026: Private Equity, Clearer View, Tougher Terrain, February 10, 2026.
ToltIQ, ToltIQ Selected by H.I.G. Capital for Firm-Wide Due Diligence Deployment, March 6, 2026.
PwC, Reimagining Deals for the Age of AI: PwC and ToltIQ Enter Strategic Relationship, June 15, 2026.
PitchBook, PitchBook Launches an Agentic Platform Assistant and New PitchBook Data MCP, October 14, 2026.
Datasite, Datasite Partners with Hebbia to Accelerate AI-Powered Due Diligence, October 20, 2026.
9fin, Track Private Names in Your Portfolio, Alongside Public Credits, with Private 9fin, August 27, 2026.
U.S. Securities and Exchange Commission, Private Secondary Markets, September 4, 2024.
BlackRock Aladdin, BlackRock Aladdin Drives Private Markets Transparency, February 5, 2026.
Disclosures & Disclaimers
This blog post is provided by Axis Group Ventures for informational and educational purposes only. It does not constitute investment, legal, accounting, or tax advice and should not be relied upon as such. Nothing contained herein should be interpreted as an offer to buy or sell any securities. Any actual offer or solicitation will be made exclusively through formal documentation provided by the relevant issuer or seller.
Axis Group Ventures is not a registered broker-dealer and does not execute, negotiate, or recommend the purchase or sale of securities. Any introductions or private-market support provided by Axis Group Ventures are conducted strictly in an advisory and consulting capacity. Readers should conduct their own due diligence and consult qualified professionals before making any financial decisions.
Investments in private securities involve significant risks, including the potential loss of the entire investment, and are typically illiquid. Past performance does not guarantee future results.
This blog post was developed using Axis Group Ventures’ proprietary market knowledge, professional insights, and years of industry experience, supplemented by large language model technology to assist with drafting and editorial refinement.
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