The world of high-stakes mergers and acquisitions has long been defined by a paradox of scale. While the deals themselves often involve billions of dollars, the preliminary research required to greenlight such transactions remains one of the most inefficient, labor-intensive, and prohibitively expensive bottlenecks in the financial sector. For decades, the "Big Three" management consultancies—McKinsey & Company, Boston Consulting Group (BCG), and Bain & Company—have maintained a virtual oligopoly on commercial due diligence, charging astronomical fees for reports that can take weeks to compile. However, a seismic shift is underway as a new generation of AI-native startups, led by DiligenceSquared, begins to automate the most grueling aspects of deal-making research.
At the heart of every private equity (PE) transaction lies the commercial due diligence (CDD) phase. This process involves a forensic examination of a target company’s market position, competitive advantages, and, perhaps most importantly, its relationship with its customers. To gain these insights, junior associates at top-tier consulting firms spend hundreds of hours conducting phone interviews with C-suite executives and procurement officers, transcribing those conversations, and synthesizing the findings into 200-page slide decks. The cost for such an engagement typically ranges from $500,000 to over $1 million.
The financial risk for private equity firms is exacerbated by the fact that these expenses are "sunk." If a deal falls through during the final stages of negotiation—a common occurrence in the volatile world of corporate finance—the PE firm is still responsible for the consultants’ fees, with no hope of reimbursement. Consequently, many firms wait until they have near-certain conviction in a deal before commissioning high-level research. This hesitation creates a strategic blind spot, as firms may pass on viable targets or pursue flawed ones simply because they lacked the budget to conduct deep-dive research earlier in the pipeline.
DiligenceSquared, a startup emerging from Y Combinator’s Fall 2025 cohort, aims to solve this "conviction gap" by leveraging generative AI and sophisticated voice agents to conduct customer interviews. By automating the data collection and initial synthesis phases, the company claims it can deliver consultancy-grade insights for $50,000—a tenth of the price of traditional competitors.
The company’s pedigree suggests it understands the nuances of the industry it seeks to disrupt. Co-founders Frederik Hansen and Søren Biltoft are not merely technologists; they are veterans of the very institutions they are now challenging. Hansen previously served as a principal at Blackstone, the world’s largest alternative asset manager, where he was responsible for commissioning the multi-million-dollar reports that DiligenceSquared now seeks to replace. Biltoft spent seven years within BCG’s private equity practice, overseeing the exact type of diligence efforts that his new venture is automating. Rounding out the leadership team is Harshil Rastogi, a former Google engineer who provides the technical backbone necessary to deploy AI in a high-security, high-accuracy environment.
The core of DiligenceSquared’s offering is its AI voice agents. Unlike the rudimentary chatbots or IVR systems of the past, these agents are designed to conduct nuanced, professional conversations with high-level corporate stakeholders. In the context of M&A, an interview isn’t just about checking boxes; it’s about probing for hidden risks, understanding why a customer might churn, and identifying the "stickiness" of a product. The startup’s technology is capable of navigating these complex dialogues, extracting critical qualitative data that was previously the exclusive domain of human analysts.
This move toward automated interviewing is part of a broader trend in the research industry. Startups such as Keplar, Outset, and Listen Labs have already begun applying AI to consumer market research. Listen Labs, for instance, recently secured $69 million in funding at a $500 million valuation for its platform that conducts interviews for brands like Microsoft and Sweetgreen. However, DiligenceSquared argues that the requirements for private equity are fundamentally different from consumer sentiment analysis. While a consumer researcher might want to know why someone prefers one brand of soda over another, a PE researcher needs to understand the technical integration of an enterprise resource planning (ERP) system or the contractual nuances of a multi-year service agreement.
To bridge the gap between AI-generated data and the executive-level analysis required for a billion-dollar buyout, DiligenceSquared employs a "human-in-the-loop" model. While AI agents handle the bulk of the interviewing and transcription, senior human consultants—often with backgrounds similar to the founders—verify the findings and ensure the commercial insights are accurate and actionable. This hybrid approach addresses one of the primary concerns of the PE industry: the "hallucination" problem inherent in large language models. In M&A, a single factual error can lead to a disastrous investment decision, making human oversight a non-negotiable component of the process.
The market has responded quickly to this value proposition. Since its launch in late 2024, DiligenceSquared has already completed projects for some of the world’s largest private equity firms and mid-market funds. This rapid traction recently culminated in a $5 million seed round led by Relentless, a new venture capital firm founded by former Index Ventures partner Damir Becirovic. The investment signals a growing belief among VCs that the "service-as-software" model—where AI performs tasks traditionally billed as professional services—is the next frontier of enterprise technology.
The implications of this shift extend far beyond cost savings. By lowering the entry price for high-quality due diligence, DiligenceSquared is effectively democratizing the M&A process. Smaller mid-market funds, which may have previously lacked the capital to hire BCG or McKinsey for every deal, can now access the same level of market intelligence. Furthermore, even the largest firms are changing their behavior. With research costs dropping from $1 million to $50,000, PE firms are commissioning diligence reports much earlier in the deal lifecycle. This allows them to "fail fast" on poor prospects and double down on winners with a level of data-backed confidence that was previously impossible.
However, DiligenceSquared is not alone in its quest to modernize the M&A landscape. The sector is becoming increasingly crowded as investors realize the potential for disruption. Bridgetown Research, perhaps the company’s most direct competitor, recently raised a $19 million Series A round co-led by industry titans Accel and Lightspeed. As these platforms vie for dominance, the traditional consulting firms face a difficult choice: they must either adopt these AI tools themselves—potentially cannibalizing their own high-margin billable hours—or risk being undercut by leaner, faster startups.
Looking ahead, the automation of commercial due diligence is likely just the beginning. The M&A process involves several other "silos" of research, including legal due diligence (reviewing thousands of contracts for change-of-control clauses), financial due diligence (Quality of Earnings reports), and technical due diligence (auditing software codebases). Each of these areas is currently dominated by high-priced specialists and is ripe for AI-driven disruption.
As generative AI continues to evolve, we may see the emergence of "autonomous deal rooms," where AI agents handle everything from initial outreach and market mapping to the final drafting of purchase agreements. In such a future, the role of the human investment professional will shift from data gathering to pure strategy and relationship management.
For now, the success of companies like DiligenceSquared serves as a powerful case study for the "vertical AI" movement. Rather than building general-purpose tools, these startups are targeting specific, high-value workflows in industries characterized by high costs and manual labor. By combining deep domain expertise with cutting-edge technology, they are proving that even the most prestigious and entrenched sectors of global finance are not immune to the transformative power of artificial intelligence. The $1 million consulting report may not disappear overnight, but its days as the industry standard appear to be numbered.