AI M&A Research Startup Just Made Due Diligence Affordable
AI-powered M&A research is finally within reach for private equity firms of all sizes. DiligenceSquared, a Y Combinator-backed startup, is using artificial intelligence and voice agents to deliver commercial due diligence reports at a fraction of what traditional consultancies charge — and the industry is already paying attention. The company just closed a $5 million seed round, signaling that Wall Street's most rigorous research process is about to get a serious technological overhaul.
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| Credit: DS2 |
Why M&A Due Diligence Has Always Been Brutally Expensive
Mergers and acquisitions are among the most complex transactions in business. Even for the largest, best-staffed private equity firms on the planet, the due diligence process is a marathon of spreadsheets, strategy sessions, and staggering advisor fees. Before any deal closes, firms must assess market dynamics, evaluate the target company's competitive positioning, scrutinize financials, and validate growth assumptions — all under intense time pressure.
To handle the commercial research side of that work, PE firms have historically turned to elite management consultancies. Firms dispatch teams of seasoned advisors to produce detailed reports on market size, customer sentiment, competitive threats, and growth potential. This kind of top-tier commercial diligence doesn't come cheap. Firms routinely spend millions of dollars on a single engagement, and that's before factoring in legal and accounting fees.
The financial sting is made worse by a painful reality: if a deal falls through — which happens often — those advisory costs are simply gone. There's no reimbursement, no credit, no consolation. That risk leads many PE firms to delay hiring outside consultants until they're nearly certain a deal will proceed, which can slow decision-making and mean that promising targets get evaluated too late or too shallowly.
The Hidden Cost Nobody Talks About: Deals That Die Before Research Begins
Here's the part that rarely makes headlines: many potentially valuable acquisitions never get a thorough look simply because the research cost is too prohibitive at early stages. Mid-market funds, in particular, often lack the budgets to commission McKinsey-level commercial diligence on every deal they're considering. So they cut corners, rely on internal analysis, or skip the deep-dive altogether.
That creates blind spots. Firms sometimes move forward without fully understanding the competitive landscape or customer retention dynamics of a target. Other times, they pass on a deal that could have been a strong performer — not because the fundamentals were bad, but because they couldn't afford to find out. This is the structural inefficiency that DiligenceSquared was built to fix.
Meet the Founders Who Know This Problem From Both Sides
DiligenceSquared wasn't built by outsiders looking in. Co-founders Frederik Hansen and Søren Biltoft bring rare, complementary expertise to the problem — one from the buyer's side, one from the advisor's.
Hansen previously served as a principal at Blackstone, one of the world's most prominent private equity firms. In that role, he commissioned commercial due diligence reports for multiple billion-dollar buyouts. He understands exactly what PE firms need from that research, what makes a report actionable, and where traditional consultancies fall short. Biltoft, meanwhile, spent seven years at BCG working within its private equity practice — directly leading the kinds of commercial diligence efforts that Hansen was commissioning on the other side of the table.
Together, they've sat in nearly every seat at the M&A research table. That dual vantage point gives DiligenceSquared a credibility that purely tech-driven competitors would struggle to match. They're not guessing at what PE firms want. They've spent careers delivering it — and being disappointed by it.
How AI and Voice Agents Are Changing the Research Game
So what does DiligenceSquared actually do differently? The startup uses a combination of artificial intelligence and voice agents to automate and accelerate the most labor-intensive parts of commercial due diligence. Traditional consultancy engagements involve weeks of expert interviews, customer surveys, competitive mapping, and synthesis work — all performed by teams of high-billing consultants.
DiligenceSquared compresses that timeline dramatically. Its AI-powered platform can conduct structured interviews at scale using voice agents, synthesize responses, map competitive landscapes, and generate research outputs that rival what a top-tier consultancy would produce — but in a fraction of the time and at a fraction of the price. The result is consulting-quality commercial research that's accessible not just to the largest buyout shops, but to mid-market funds that previously couldn't afford it.
This isn't about replacing human judgment entirely. It's about removing the parts of the process that don't require human judgment — scheduling, repetitive interviewing, data aggregation, initial synthesis — so that the high-value thinking can happen faster and more affordably.
Early Traction With the World's Largest PE Firms
DiligenceSquared launched in October 2025 as part of Y Combinator's Fall 2025 cohort. In the months since, the startup has already completed multiple projects for several of the world's largest private equity firms, as well as mid-market funds. That early commercial traction is a strong signal — these firms are notoriously selective about the partners they trust with sensitive deal research.
Getting a foothold with large PE firms this quickly is no accident. Hansen's and Biltoft's networks and reputations opened doors that a typical startup would take years to crack. And once inside, the quality of the work appears to have done the rest. Repeat business and referrals in the private equity world are the clearest indicator of genuine product-market fit.
A $5 Million Seed Round Led by a Former Index Ventures Partner
That early momentum caught the attention of Damir Becirovic, a former partner at Index Ventures — one of Europe's most respected venture capital firms. Becirovic led DiligenceSquared's $5 million seed round out of his new venture vehicle, a strong vote of confidence from someone with deep pattern recognition in enterprise software and fintech.
A $5 million seed at this stage reflects belief not just in the product, but in the market timing. Private equity has been slower than other industries to adopt AI-native tools, largely because the stakes are so high and the sensitivity of deal information is extreme. But that conservatism is beginning to crack, and DiligenceSquared is positioned to be among the first beneficiaries of that shift.
Why This Matters for the Future of Private Equity Research
The broader implication here goes well beyond one startup's funding announcement. DiligenceSquared's emergence signals a coming wave of AI-native tools specifically designed for the M&A process. Due diligence is just one slice of that workflow. Valuations, portfolio monitoring, LP reporting, deal sourcing — all of these processes carry similar inefficiencies, similar cost burdens, and similar opportunities for AI to deliver step-change improvements.
For private equity as an industry, the question is no longer whether AI will reshape deal research. It's how fast, and who will lead it. DiligenceSquared is making a credible early case that the answer starts with commercial due diligence — the research that's always been too expensive to do right, until now.
DiligenceSquared is tackling one of private equity's most persistent and costly pain points with a pragmatic, experience-backed approach. By combining genuine domain expertise with purpose-built AI, the startup is making top-tier M&A research affordable for firms that previously had to choose between quality and cost. With $5 million in fresh capital, a growing client list, and founders who have lived this problem from both sides of the table, DiligenceSquared looks like one of the more compelling early-stage bets in the enterprise AI space right now.
