Preface
ToltIQ is positioning itself at the intersection of generative AI and private markets, with a focus on making diligence more efficient, accurate, and scalable. For the GP Stakes community, where small teams are tasked with analyzing complex documents under time pressure, the platform offers a potentially transformative workflow. We spoke with Ed, Founder and CEO of ToltIQ and former KKR executive, about how the firm is tailoring its technology to GP Stakes firms.
What We Already Know
Complex diligence: The GP Stakes investment process mirrors traditional private equity workflows but adds complexity: layered fund structures, multiple reporting formats, and dense PPMs with bespoke terms
Manual effort required: Dealmaking professionals have historically been harmonizing information across funds and strategies by manually downloading, parsing, and reconciling PDFs and spreadsheets
Little change over decades: While data rooms have gone virtual, the fundamental challenge of extracting and analyzing structured insights from unstructured documents has remained largely unchanged
Digging Deeper
Bringing AI to GP Stakes Diligence
ToltIQ’s platform applies retrieval-augmented generation (RAG) and a library of diligence-specific workflows to accelerate the review of financial, commercial, and legal documents. Underlying the system are multiple large language models from OpenAI, Anthropic, and Gemini (currently in beta), which ToltIQ’s research team is continuously testing. This ensures that the default models powering document decomposition and collaborative analysis are the strongest available for diligence, while still giving users the option to switch models based on their own preferences. For GP Stakes firms, this means:
Harmonization of formats: Credit and private equity arms within the same GP often report differently. ToltIQ allows teams to normalize disparate data sources (GP Stakes firms portfolio GPs often have different reporting methods)
Extraction from complex documents: PPMs, waterfall models, and clawback provisions can be analyzed for key terms and scenarios
Workflow optimization: The platform supports back-and-forth collaboration, enabling deal teams to move faster while maintaining accuracy
Guardrails against hallucination: By limiting the scope of models to curated documents, ToltIQ emphasizes citations, transparency, and reliability
Rather than reinventing the investment process, the firm aims to meet professionals where they are—augmenting their work and accelerating time to insight.
Case Study: Investcorp Strategic Capital Group
Investcorp was among ToltIQ’s earliest clients. The relationship began with a review of legacy deals, where the platform compressed an eight-week diligence cycle into about one week. By iterating on questions, prompts, and outputs, Investcorp was able to build libraries that now streamline future processes. What once required associates to manually cut and paste data from 50-page CIMs can now be completed in minutes.
The collaboration extended beyond retrospective analysis. Investcorp has since leveraged ToltIQ in evaluating new inbound opportunities, parsing PPMs, and identifying patterns in quarterly portfolio updates. In one instance, the platform highlighted other firms marketing themselves with similar claims of uniqueness—a reminder that pitch decks often blur the line between fact and positioning. According to Ed, these workflows consistently delivered 85–95% accuracy, enabling professionals to focus on judgment and decision-making rather than rote extraction.
Implications for GP Stakes Investors
The potential impact for GP stakes firms is twofold. First, efficiency: smaller teams gain leverage by reducing the time required to extract and structure information. Second, scalability: as check sizes increase from $50–100 million to $200 million and above, the volume and complexity of documents only grow. ToltIQ aims to give firms confidence that they can keep pace without expanding headcount proportionally.
Accuracy remains a central concern. While the system is not positioned as a "100% solution," Ed, as mentioned above, argues that 85–95% accuracy with transparent citations is sufficient to shift workflows. Over time, he expects autonomous agents and improved model capabilities to raise that bar closer to 100%, particularly for multi-level hierarchical documents like fund waterfalls or detailed credit agreements.
For GP stakes investors, the message is clear: the diligence process need not remain frozen in time. With adoption growing—30 commercial clients and 35 in pilot—the industry is beginning to recognize that AI tools can augment human expertise without replacing it. In a business where differentiation often comes from speed, insight, and precision, ToltIQ is staking its claim as an enabler of the next phase of GP stakes diligence.
Beyond the Office
We always enjoy learning what members of the GP Stakes community are passionate about outside of work. For Ed, that means exploring U.S. National Parks in the off-season, off-grid, and in the company of his five-year-old Belgian Malinois, Sadie. Each trip is part of an extended road journey, often cross-country, with hikes capped at around 10 miles. He has set a goal of visiting 50 National Parks—having already completed 27, with 23 left to go.
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