ApexMake vs DiligenceSquared
One replaces the consulting project. The other replaces the wait between having a question and having an answer. They barely compete, but buyers keep putting them side by side.
- Shape: a $50K project per target, against an always-on subscription
- Interviewees: the target's own customers, against sector operators
- Timeline: weeks per report, against roughly 6 seconds per question
The short answer
- DiligenceSquared is an AI-augmented commercial due diligence firm founded by alumni of Blackstone, BCG and Google. It raised a $5M seed led by RELENTLESS with Y Combinator participating in March 2026.
- The model is a consultant-led, AI-executed project. Senior consultants scope the research plan, AI voice agents interview the target's customers and competitors at scale, and the output is an interactive report where every finding traces back to its source transcript.
- It replaces work private equity funds have historically bought from McKinsey, BCG or Bain for $500K to $1M, at roughly $50,000 a project. Clients reportedly represent more than $3T in combined AUM, including several of the ten largest PE funds.
- ApexMake is not a project. It is an always-on expert knowledge base. Operators are interviewed by an AI agent in advance, and analysts query them in plain language for a cited answer in about six seconds, at any hour, as often as they want. Live calls with the same operators are included for when a conversation beats a query.
- DiligenceSquared is right when the deliverable is a full commercial diligence report on a specific target. ApexMake is right when you need continuous, sourced expert intelligence before the CDD is commissioned, during it, and long after it is filed.
Which should you use?
These two answer different questions, and any honest comparison starts there. DiligenceSquared compresses a seven-figure consulting engagement into a $50K AI-executed project, which is a real unbundling of MBB. But it is still a project: scoped, commissioned, executed over weeks, delivered as a report about one target. ApexMake is infrastructure rather than an engagement. It handles the hundred questions that come up before you decide a target deserves a diligence project at all, the ones that surface mid-process, and the ones that appear in monitoring afterwards. Most funds that use both will use them at different points in the same deal.
What sets ApexMake apart
| Dimension | ||
|---|---|---|
| What you buy | Continuous access to a queryable expert knowledge base, plus calls with the operators behind it | A scoped commercial diligence project, delivered as an interactive report |
| Who gets interviewed | Vetted operators, engineers and executives recruited into the network in advance | The target's customers, competitors and market experts, recruited per project |
| When the interview happens | Before you ask, which is what makes six-second answers possible | After the engagement is scoped, as part of the project |
| Time to insight | About 6 seconds per question, 24/7 | A project timeline. Dramatically faster than MBB, still measured in weeks. |
| Scope | Any question inside the covered sectors, unlimited follow-ups | One target, one market, one defined research plan |
| Human involvement | AI-led corpus answers, plus direct calls with pre-vetted operators when wanted | Senior consultants scope, validate and synthesise. AI executes the gathering. |
| Pricing shape | Subscription, with marginal cost per question near zero | Roughly $50K per project |
| Traceability | Every claim cites the named expert behind it, with an audit trail on every query | Every finding traces to the original interview transcript in the report |
| Where it sits in a deal | Screening, pre-LOI, live diligence, IC prep and post-close monitoring | Confirmatory commercial diligence between LOI and investment committee |
What DiligenceSquared is
- 2025, by Frederik Hansen (ex-Blackstone), Soren Biltoft (ex-BCG) and Harshil Rastogi (ex-Google). Headquartered in New York.
- AI-augmented commercial due diligence. Consultant-led research plans, AI voice agents conducting interviews, senior consultants validating and synthesising into an interactive report.
- $5M seed led by RELENTLESS with Y Combinator participating, March 2026.
- Clients reported to represent more than $3T in combined assets under management, including 5 of the 10 largest private equity funds.
- Roughly $50,000 per project, positioned against the $500K to $1M MBB firms charge for comparable commercial due diligence.
- Commercial due diligence, voice-of-customer research, competitive intelligence, market sizing, new market and product entry. Fully traceable findings with MCP integration into client environments.
Unbundling MBB is a different job from unbundling the expert call
DiligenceSquared is aimed squarely at the commercial due diligence line item. A PE fund evaluating a target has historically paid McKinsey, BCG or Bain somewhere between $500K and $1M to interview the target's customers, size the market, map competition and produce a report the investment committee can rely on. DiligenceSquared does the same work with AI voice agents doing the interviewing and senior consultants doing the scoping and synthesis, for around $50,000.
That is a real unbundling, and the traction shows it. Clients reportedly representing more than $3T in combined AUM, including several of the largest funds in the world.
ApexMake is unbundling something else, the expert call. The question is not who writes the diligence report. It is why getting one expert's answer to one question takes days and costs a four-figure sum. Both are AI-native attacks on the cost of investment research, aimed at different line items.
Project economics versus always-on economics
A $50K project is transformative pricing against a $750K consulting engagement. It is still project pricing, which means the same gating behaviour applies. You commission it once you have decided a target is worth serious money, and the questions that come up before that decision have to be answered some other way.
In practice that other way is where most diligence questions live. Screening a pipeline, sanity-checking a thesis, pressure-testing a management claim mid-process, monitoring a portfolio company after close. None of those justify commissioning a project, and all of them generate questions that need real operator knowledge.
ApexMake is priced as infrastructure rather than as an engagement because that is the shape of the demand. The marginal question costs effectively nothing, which is what lets a team ask the fifty questions that never justified a project.
Who is on the other end of the interview
This is the most under-appreciated difference between the two. DiligenceSquared's AI agents interview the target's own customers and competitors, voice-of-customer research run at scale. That is the right population for confirmatory commercial diligence, because you want to know whether this specific company's customers will renew.
ApexMake interviews operators, engineers and executives with senior first-hand experience in a sector, and keeps them in a network. That is the right population for a different question: how the technology actually behaves, what vendor economics really look like, what breaks at scale, why contracts like this churned historically.
Neither population substitutes for the other. A target's customers cannot tell you how custom silicon behaves under production latency. A former systems engineer cannot tell you whether this particular target's top ten accounts are happy. Deals need both, at different moments.
Traceability, converged
Both companies reached the same conclusion about output independently. If AI produced the finding, the finding has to be traceable, or nobody in an investment committee will rely on it.
DiligenceSquared delivers an interactive report where every finding links back to the original interview transcript, with MCP integration so the report is reachable from a client's own environment. ApexMake returns answers where every claim links to the named expert and source behind it, plus a view of where experts agree and where they diverge.
The convergence says something about where the category is heading. The differentiator in AI research is no longer generating a plausible answer. It is producing one that survives being interrogated.
The compliance dimension
A commissioned diligence project is a bounded engagement with a defined scope and a defined counterparty, and it gets governed accordingly. An always-on expert network touches far more interactions and needs controls that scale with them.
So ApexMake screens for material non-public information on every query and every follow-up, runs conflict checks on every expert match, and logs each step with a timestamp into an audit trail. The control operates continuously rather than per engagement.
For funds using both products that is complementary rather than competing. The project gets project governance, and the ongoing research layer gets continuous governance.
Choosing between them
DiligenceSquared fits better when
- The deliverable is a full commercial due diligence report on one specific target, for an investment committee.
- You need voice-of-customer research with the target's actual customers, at scale, rather than sector-level operator knowledge.
- You want senior consultants owning scoping and synthesis, and a report you can circulate.
- You are replacing an MBB line item and the economics of $50K against $750K are the whole point.
ApexMake fits better when
- You need answers continuously, while screening, mid-process and after close, not once per commissioned project.
- The question is technical: AI infrastructure, inference, training, or the software and infra stack around them.
- The answer is needed in seconds, at any hour, without scoping an engagement.
- Your team's question volume runs far higher than any project-based model can absorb.
- Compliance needs MNPI screening and an audit trail on every interaction, not only on commissioned work.
- You want both modes on tap: instant corpus answers, and operator calls booked in a couple of clicks.
ApexMake vs DiligenceSquared, explained
Is DiligenceSquared an expert network?
Not exactly. It is an AI-augmented commercial due diligence firm. AI voice agents conduct interviews with a target's customers, competitors and market experts, and senior consultants validate and synthesise the results into an interactive report. The output is a research deliverable rather than access to a network.
Can ApexMake replace a commercial due diligence project?
For many questions, yes, particularly technical and sector-level ones, and particularly early, before a project would be commissioned. For a confirmatory, target-specific report built on interviews with that company's own customers, a CDD provider is doing a different job.
How much does DiligenceSquared cost?
Roughly $50,000 per project, positioned explicitly against the $500K to $1M that McKinsey, BCG and Bain charge for comparable commercial due diligence work.
Both use AI to interview people, so what is actually different?
Who gets interviewed, and when. DiligenceSquared interviews a target's customers and competitors after a project is scoped. ApexMake interviews sector operators and engineers in advance and keeps them in a queryable network, which is why an answer takes about six seconds rather than a project cycle.
Would a fund use both?
Frequently. ApexMake covers the continuous questions, from screening and thesis work to mid-process checks and post-close monitoring, and a CDD provider covers the bounded, target-specific report the investment committee requires.
Sources
Figures describing DiligenceSquared are drawn from the company’s own published materials and third-party reporting, and were accurate as of the date of publication. Product and pricing details change; verify with the provider before making a purchasing decision.