How Work Gets Done
Six of the ten largest private-equity funds globally have bought diligence reports from a team that launched in October 2025.
By March 2026 the company had closed a $5 million seed round led by RELENTLESS with Y Combinator participation and completed engagements for firms representing more than $2 trillion in combined assets under management. Headcount stood at eight per BuiltIn; LinkedIn shows five founding hires joined in a recent cohort: two engineers from Neon, Visa, and Stanford; two business leads from BCG and McKinsey with Wharton and Duke MBAs; and a founder associate from Blackstone Growth Equity now at Harvard Business School. The team works full-time from Hudson Yards.
The workday resembles neither a startup nor a consultancy. It looks like what happens when you force both models into the same room and refuse to let either dominate. Engineers and consultants build the product side by side — not as a slogan, but as the only way the workflow functions. The platform moves through six stages: research plan (consultant-led), data gathering (AI-led), data validation (AI plus consultant), analysis (AI-led), review and synthesize (consultant-led), interactive report (platform). Each engagement uses AI voice agents to run dozens or hundreds of expert interviews in parallel, then senior consulting judgment to verify nuance and commercial relevance. The synthesis step transforms thousands of datapoints into market views in hours instead of weeks. Every claim in the final report links back to its source transcript; clients can interrogate findings live or pull them into their own environment via MCP. There is no black box.
The "no analyst pyramid" principle means the team carries no junior layer. Everyone has bought this research at Blackstone, sold it at BCG, or built AI systems at Google, or joins with equivalent depth. Everyone owns work end to end. Decisions move fast because the people who understand the customer problem also write the code that solves it, or sit beside the person who does.
Pace is dictated by deal cycles, not sprint calendars. The soccer team (five employees plus friends, rainy 7-5 loss to last season's finalists in September) is the only scheduled social ritual. The rest is demand. "Strong demand and incredible feedback from our customers," a July LinkedIn post said. The founders' background created the initial pull: Hansen spent six years at Blackstone rising to Principal across San Francisco, New York, and London purchasing the very reports DiligenceSquared now automates; Biltoft led commercial due diligence at BCG for seven years; Rastogi built fraud-detection AI at Google Ads for a decade. They knew exactly where the inefficiencies lived. The product reflects that precision: no feature exists because it seemed cool; every feature exists because a deal team needed it yesterday.
The tension is sustainable only while the team stays small and senior.
The Operating Contract
DiligenceSquared's operating philosophy reads like a contract its founders wrote for themselves before they wrote it for customers. The team page states it plainly: "Operators building the firm they wish they'd had."
The founding insight wasn't abstract. Hansen describes leaving Blackstone with "the memory of a specific purchase": a diligence report that was slow because skilled people did coordination work by hand, costly because junior associates logged brute-force hours, and opaque because the final slide sat several steps from the source material.
His response was to separate what the fee actually bought: "Some parts bought rigor. Some bought coordination. Some bought the brute force of many junior hours. Software could attack the last two. The first had to be protected." That distinction — rigor versus coordination versus volume — became the product architecture.
The six-step workflow makes the division explicit. Steps one, three, and five are consultant-led: research plan, data validation, review and synthesis. Steps two, four, and six are AI-led or platform-mediated: data gathering, analysis, interactive report.
The company summarizes it as "Human judgment, machine scale: AI gathers and structures evidence at a scale no team can staff. Senior consultants frame the questions that matter and challenge every finding before it ships."
Hansen said: "If the work is not genuinely insightful or only 90% of the way there, it simply isn't good enough for top PE firms." The company does not merely promise a larger pile of machine-made research. It promises a workflow in which automation handles scale while people remain accountable for the quality of the conclusion.
Traceability is the operational expression of that accountability. The site promises "every claim links back to the interview or dataset behind it. There is no black box — clients can interrogate findings live in the platform, or work with them in their own environment via MCP."
The YesPress profile frames it as principle: "Traceability is more than a tasteful product detail. Generative systems are fluent even when wrong, while investment research is valuable only when a team can challenge it."
The design rule is simple: let software multiply interviews and organize evidence; keep experienced people on the questions, validation, and final judgment; preserve the path from conclusion to source.
Cost discipline is framed as access, not compromise. "A fraction of the cost: Institutional-grade research for the many decisions that never justified a seven-figure consulting fee, without lowering the bar."
The vision statement extends it: "We are setting a new standard for investment research: every claim traceable, every engagement senior-led, and true rigor available for every decision, not only the ones that could justify a seven-figure fee."
Hansen told TechCrunch the lower price point means "PE firms are now far more willing to engage DiligenceSquared earlier in the process, well before they have high conviction in a deal." The strategic effect is timing; the mechanism is price.
The company's self-description as "an AI-native consulting firm" carries a deliberate tension. The YesPress profile calls its larger idea "almost old-fashioned. Important claims deserve scrutiny. Senior judgment is scarce. Evidence should survive the journey into a conclusion."
AI makes the economics work by conducting more interviews and processing more material than a human team could on the same clock. But the danger, the same piece notes, is that "more research can become more noise, delivered at machine speed."
Hansen's answer is a system of checks: human direction, automated gathering and analysis, human review.
The company's future, the profile concludes, "depends on whether it helps investors ask the right ones."
The Hiring Bar
DiligenceSquared's hiring process extends its founding DNA: three operators who have bought and sold the very product they're now building, running a sub-50-person team with no recruiter, no HR function, and a seven-day interview loop that moves at founder speed.
The company screens for two things above all: relevant experience and the ability to operate autonomously.
What "relevant experience" means here is specific. The founding team, consisting of Hansen (six years at Blackstone, rising to Principal on billion-dollar buyouts), Biltoft (seven years at BCG's Private Equity practice leading commercial diligence), and Rastogi (a decade at Google, most recently leading AI fraud detection in Ads), built the company because they knew exactly what "good" looks like in investment-grade research.
Their website states it plainly: "By domain experts, for domain experts: Our team brings experience from top-tier firms in finance, consulting and tech. We know what good looks like, and have designed workflows for investor-grade accuracy that stands up to an investment committee."
Job postings on Zero G Talent reflect this seniority:
| Role | Salary Band |
|---|---|
| Founding Engineer (Frontend/Backend/Fullstack) | $160K–$200K |
| Head of GTM | $190K–$220K |
| Senior Account Executive | $140K–$190K |
| Marketing Lead | $110K–$150K |
| GTM Associate | $140K–$175K |
Autonomy is the other non-negotiable. With no process infrastructure, every hire inherits broad remit and direct founder access. TheAntiJobBoard notes that "working at a B2B analytics company at this stage means broad remit, direct access to founders, and equity that still means something if the company works out."
Cold outreach to founders is explicitly how candidates get seen: applications go to a founder juggling everything.
Biltoft has said publicly that "investors won't adopt tools that are 80–90% correct" — the quality bar for the product is investment-committee grade.
Who Stays, Who Leaves
The profile that succeeds at DiligenceSquared is unusually specific, and the company makes little effort to broaden it. The careers page states it plainly: "No analyst pyramid: A senior team where everyone has done the work and owns it end to end."
The team is 11–50 people as of late 2026, and the median salary on the Zero G Talent board sits at $200K with a band of $140K–$200K, compensation that reflects an expectation of immediate, autonomous contribution.
Who thrives? First, hybrid practitioners: engineers who understand private-equity workflows, or former investors and consultants who can write production code. The company describes itself as "a team of investors, consultants and engineers automating the market research that funds and corporates pay millions for," and the operating principle "Engineers and operators, together: Engineering and consulting build the product side by side, in the same room" is the daily reality.
Second, people who treat "investor-grade accuracy" as a non-negotiable constraint rather than a slogan. The platform replaces $500K–$1M McKinsey/BCG/Bain engagements and serves six of the ten largest PE funds managing $4T+ in combined AuM, according to DiligenceSquared's website. Every claim in a DiligenceSquared report must be traceable to a source interview or dataset ("no black box," as the site puts it).
Third, operators who want "real ownership: A mandate to shape the product, the process and the firm," and who understand that mandate comes with no safety net. The company moved to Hudson Yards in early 2026 and has been "growing fast" since, per the founders' LinkedIn updates.
Who burns out? Specialists who refuse to cross the aisle. A pure researcher who wants to publish papers on voice-agent architectures will chafe at the product deadlines. A pure salesperson who cannot explain how the AI synthesis pipeline handles hallucination will lose credibility with the technical buyers (PE principals and operating partners) who evaluate the platform. People who need hierarchy to feel oriented (the "analyst pyramid" refugees) will find the flat, senior-only structure disorienting rather than liberating. And anyone who treats the 80–90% accuracy threshold as acceptable will be pushed out by peers who treat it as a failure mode.
The tension is real: the company sells speed and cost reduction to PE funds, but delivers it through a labor-intensive, high-judgment hybrid of human experts and AI agents. That contradiction — "the rigor of a premier advisory firm, the speed of a machine" — is the daily work. The people who stay are the ones who find that contradiction energizing rather than exhausting.
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