Most private equity due diligence is conducted by asking the company being acquired to introduce you to its happiest customers. This is roughly as rigorous as evaluating a restaurant by interviewing only the chef's mother.
The Confidence Game
There is a peculiar ritual in private equity that has survived decades of professionalisation, billions of dollars in advisory fees, and the collective intelligence of some of the sharpest financial minds on the planet. It goes like this: a PE firm considers acquiring a company, hires a strategy consultancy to validate the market opportunity, and the consultancy proceeds to interview a carefully curated selection of customers that the target company has handpicked for the occasion.
The customers, naturally, say encouraging things. They are, after all, the company's best relationships. The ones who churned last quarter are not on the list. The ones who signed under duress during a competitive RFP and have been quietly evaluating alternatives are not on the list. The ones whose contract renewal is contingent on a feature that does not yet exist are emphatically not on the list. What remains is a sample so thoroughly pre-filtered for positivity that it would make a Soviet election commissioner blush.
The consultancy then writes this up in a 200-page report, charges somewhere between $50,000 and $200,000 for the privilege, and delivers it four to eight weeks later. The PE firm's investment committee reads the executive summary, notes that "customer sentiment is broadly positive," and factors this into a decision involving tens or hundreds of millions of dollars.
This is not a caricature. It is standard practice. And it persists not because the people involved are foolish, but because until very recently there was no practical alternative. Independent consumer research at the speed and scale required for deal timelines was simply not available. You could have rigour or you could have speed. You could not have both.
That constraint has now been removed. Synthetic research, and specifically the capacity to assemble independent consumer panels in hours rather than weeks, has introduced a genuinely new option into the due diligence toolkit. Whether the industry adopts it enthusiastically, cautiously, or not at all will tell us something interesting about the relationship between financial sophistication and methodological conservatism.
The Traditional Diligence Problem
To understand why synthetic research matters for private equity, one must first appreciate the structural deficiencies of the existing approach. These are not minor quibbles. They are load-bearing flaws in the methodology that underpins investment decisions worth billions.
The Selection Bias
The most fundamental problem is respondent selection. In a typical customer diligence exercise, the target company provides a list of reference customers for the diligence team to interview. The company has every incentive to populate this list with its most satisfied, most loyal, and most articulate advocates. It has zero incentive to include detractors, fence-sitters, or customers whose experience has been mediocre.
This is not dishonesty. It is rational behaviour. The company is trying to get acquired at the highest possible valuation, and presenting its best customers is a perfectly legal way to support that objective. But the result is a sample that systematically overstates customer satisfaction, understates churn risk, and provides no visibility into the segments of the market that the company has failed to penetrate or has lost.
A diligence team relying on company-selected references is, in statistical terms, conducting research with a profoundly biased sample. The conclusions drawn from such a sample are not wrong in the way that a coin flip is wrong. They are wrong in the way that polling only people who voted for the incumbent is wrong: consistently, predictably, and in a direction that flatters the subject.
The Time Problem
Deal timelines are unforgiving. A typical leveraged buyout process runs six to twelve weeks from letter of intent to close. Within that window, the buyer must complete financial diligence, legal diligence, operational diligence, commercial diligence, and increasingly ESG diligence. The commercial workstream, which includes market validation, customer research, and competitive assessment, typically receives four to eight weeks.
Traditional market research, conducted properly, requires two to three weeks for study design, three to four weeks for fieldwork (recruiting respondents, conducting interviews, collecting survey data), and one to two weeks for analysis and reporting. The arithmetic is unfavourable. A thorough independent research programme takes longer than the deal timeline permits. Something must be compressed, and what gets compressed is usually rigour.
The practical consequence is that most commercial diligence relies on expert interviews, industry reports, and the company-curated customer references described above. Primary consumer research, if it happens at all, is limited to a handful of interviews conducted under time pressure with respondents who may or may not represent the addressable market.
The Cost Problem
A commercial diligence engagement from a top-tier strategy consultancy costs $50,000 to $200,000. This is not inherently unreasonable for the value at stake, but it creates a threshold effect. Diligence of this depth is only economically rational for deals above a certain size. For middle-market transactions, where the enterprise value might be $20 million to $100 million, spending $150,000 on a single workstream is harder to justify. The result is that smaller deals receive less rigorous diligence, which is precisely backwards: smaller companies typically have less mature operations, weaker competitive moats, and more concentrated customer bases, all of which make independent validation more important, not less.
The Expertise Bottleneck
Strategy consultancies employ smart people, but they are generalists by design. A partner at Bain who has spent the last six months advising a healthcare client may be staffed onto a CPG diligence engagement next week. The associate who writes the commercial diligence report may have graduated from business school eighteen months ago and have no direct experience in the target industry.
This is not a secret. PE firms know that consultancy teams bring analytical frameworks rather than domain expertise. But the implication is that the "expert" assessment of market dynamics, competitive positioning, and customer sentiment is often produced by people who are intelligent novices in the relevant domain. Their conclusions are only as strong as the data they are given, and that data, as established above, is structurally biased.
What Synthetic Research Changes
Synthetic consumer research, in its current form, does not solve every problem in due diligence. It does, however, address the three structural deficiencies outlined above with sufficient force to change the calculus for how commercial diligence is conducted.
Independence of Respondent Selection
The single most consequential advantage is that the target company has no involvement in selecting the research participants. When a firm like FishDog creates a synthetic consumer panel for a diligence exercise, the respondents are generated from demographic, psychographic, and behavioural parameters that the diligence team defines. The company being evaluated does not see the panel, cannot influence its composition, and has no opportunity to steer the conversation towards its strengths.
This is the methodological equivalent of switching from company-arranged reference calls to an independent blind survey. The difference in information value is not marginal. It is categorical. A panel of 10 synthetic consumers, independently constructed and independently questioned, can surface risks and objections that 50 hand-selected reference customers would never reveal, because those reference customers were selected specifically for their inability to reveal such things.
Speed
A synthetic research study can be designed, fielded, completed, and analysed in a matter of hours. Not days. Not weeks. Hours. This transforms the role of consumer research in the deal process from a luxury that the timeline rarely permits to a routine step that can be repeated as new questions emerge.
Consider the practical implications. A diligence team reviews the target's financials on Monday and notices that growth has decelerated in a particular customer segment. By Tuesday, they can have a synthetic consumer panel drawn from that segment, answering questions about purchase intent, competitive alternatives, switching barriers, and unmet needs. By Wednesday, the investment committee has independent consumer data to contextualise the financial trend. In the traditional model, that same question would require a research brief, a fieldwork plan, a recruitment period, and a reporting phase that would deliver answers approximately three weeks after the deal had already closed.
Cost
A synthetic research study costs a fraction of a traditional diligence engagement. The precise figure depends on the platform and the scope, but the order of magnitude is hundreds of dollars per study rather than tens of thousands. This eliminates the threshold effect that currently limits rigorous diligence to large transactions. A middle-market PE firm evaluating a $30 million acquisition can now conduct independent consumer validation for less than the cost of a single expert interview at a strategy consultancy.
More importantly, the low marginal cost enables iteration. Instead of commissioning one definitive study and treating its conclusions as gospel, a diligence team can run multiple studies testing different hypotheses, exploring different segments, and examining different competitive scenarios. The research becomes a conversation rather than a pronouncement.
The Diligence Use Cases
The application of synthetic research to due diligence is not a single use case but a family of related ones. Each addresses a different question that PE firms need to answer before committing capital.
Portfolio Company Customer Validation
The most direct application is validating the customer base of a potential acquisition. Does the target company's value proposition actually resonate with its purported target market? Are the problems it claims to solve genuinely felt by the consumers it claims to serve? Would those consumers choose this product over alternatives in a blind evaluation?
These are questions that company-selected reference customers are structurally incapable of answering honestly. A synthetic panel, constructed independently and questioned without the company's knowledge, can provide a materially different perspective. The result may confirm the company's claims, which is valuable reassurance. Or it may surface concerns, which is valuable intelligence. Either way, the buyer has better information than the status quo provides.
Market Sizing Validation
Every acquisition target presents a total addressable market figure. These figures are, with rare exceptions, optimistic. Not because founders and management teams are liars, but because TAM calculations involve assumptions about market boundaries, adoption curves, and willingness to pay that are inherently speculative. The number in the pitch deck is the answer to "How large could the market be if everything goes well?" rather than "How large is the market actually?"
Synthetic research can pressure-test TAM assumptions by asking representative consumers about their actual behaviour, spending, and purchase intent. How many people in the target demographic actually experience the problem that the product solves? Of those, how many currently spend money on a solution? Of those, how many would consider switching? Each layer of questioning narrows the theoretical TAM to something closer to a realistic serviceable addressable market, and does so with data rather than assumptions.
Competitive Landscape Assessment
The target company's view of its competitive landscape is, predictably, self-serving. Its pitch deck will feature a two-by-two matrix in which it occupies the desirable upper-right quadrant while competitors cluster in less attractive positions. The criteria for the axes will have been chosen specifically to produce this result.
Synthetic consumer panels can provide an independent view of competitive dynamics. How do consumers actually perceive the target relative to its competitors? Which features matter most in purchase decisions? Where does the target have genuine differentiation, and where is it interchangeable with alternatives? This information is available through traditional competitive research, but at the cost and timeline described above. Synthetic research delivers it in hours.
Customer Sentiment Pre-Acquisition
Perhaps the highest-value application is assessing customer sentiment in categories where the acquirer has limited direct experience. A PE firm evaluating an investment in a D2C skincare brand may have extensive financial modelling expertise but limited understanding of how consumers in that category think, feel, and make decisions. Synthetic panels provide instant access to a representative cross-section of the target category's consumers, allowing the diligence team to build category intuition rapidly and cheaply.
This is particularly valuable for platform PE firms that invest across multiple verticals. A firm that owns businesses in healthcare, consumer goods, and enterprise software cannot maintain deep consumer expertise in every category simultaneously. Synthetic research acts as a category-specific intelligence layer that can be activated on demand, tuned to any market, and decommissioned when the analysis is complete.
How FishDog's Diligence Workflow Operates
To make this concrete rather than theoretical, it is worth examining how the process actually works in practice. FishDog's `+diligence` workflow, designed specifically for venture capital and private equity use cases, follows a structured sequence that prioritises independence, relevance, and speed.
Phase 1: Research and Thesis
The process begins with the diligence team providing a target company's website and, optionally, a brief description of what the company does. The system then researches the startup or target company, examining its product, value proposition, target customer, competitive landscape, and pricing model.
From this research, a thesis is constructed: what the company claims to do, for whom, and why it matters. This thesis is returned to the diligence team for approval before any research is conducted. The approval step is deliberate. It ensures that the subsequent research is testing the right hypothesis rather than an AI's interpretation of what the hypothesis should be.
Phase 2: Panel Construction (The Over-Recruit Strategy)
This is where the independence advantage becomes concrete. Rather than accepting a company-curated list of customers, FishDog constructs an independent research group of synthetic consumers who match the target company's claimed customer profile.
The over-recruit strategy is central to the methodology's rigour. The system recruits 15 to 20 participants initially, then scores each by relevance based on their demographic profile, job title, and behavioural summary. Participants are classified as high, medium, or low relevance. Those scoring low are removed from the study before any questions are asked. The result is a curated panel of 10 to 12 participants who genuinely represent the target customer, selected through a process that neither the target company nor its management team has any visibility into or influence over.
This is a small but important methodological point. In traditional diligence, even when an independent research firm is commissioned, the target company typically provides input on the ideal customer profile that shapes recruitment criteria. That input subtly biases the sample towards the company's strongest segments. The over-recruit-and-curate approach mitigates this by starting broad and narrowing based on objective relevance scoring rather than company guidance.
Phase 3: Study Design
The study itself is designed around six to seven questions that validate the core investment thesis without revealing the VC context. This last point is important. If participants know that the research is being conducted for investment purposes, their responses may be influenced by social desirability bias or by a desire to appear supportive of innovation. By framing the questions as general customer research, the study elicits more honest and less performative responses.
The questions typically cover:
Problem existence and severity. Does the target customer actually experience the problem the company claims to solve? How painful is it, quantified in time or money?
Current solutions and switching barriers. What do target customers do today to address the problem? How satisfied are they with existing solutions? What would it take to make them switch?
Interest in the proposed approach. When presented with the company's value proposition (without naming the company), how do target customers react? What excites them? What concerns them?
Feature preferences and dealbreakers. Which aspects of the product matter most? Which are irrelevant? Are there dealbreakers that the company has not addressed?
Phase 4: Analysis and Output
The completed study generates a VC-friendly output that maps findings to the investment decision framework. This is not a generic research report. It is structured around the specific questions that an investment committee needs answered: Is the problem real? Is the solution viable? Is the market receptive? What are the risks?
The output includes problem validation (with severity ratings), solution receptivity scores, competitive positioning data, and explicit investment implications. A diligence team can take this directly into an investment committee meeting without the translation layer that traditional research reports typically require.
The Garnett Station Precedent
It would be irresponsible to discuss synthetic research in private equity without acknowledging that the concept is not purely theoretical. Garnett Station Partners, a New York-based consumer-focused investment firm, is among the PE and VC firms that have incorporated synthetic research tools into their diligence processes.
Simile, the Stanford and Gallup-backed synthetic research platform that raised $100 million in Series A funding from Index Ventures, counts Garnett Station among its customers. The firm uses synthetic consumer panels to supplement traditional diligence methods, providing an additional data point on consumer sentiment, brand perception, and market dynamics in the consumer categories where it invests.
The Garnett Station example is instructive for several reasons. First, it demonstrates that serious financial buyers, not just brand marketers or product managers, have concluded that synthetic research produces actionable intelligence. Second, it suggests that the value proposition extends beyond cost savings. A firm like Garnett Station is not choosing synthetic research because it cannot afford McKinsey. It is choosing synthetic research because it provides information that McKinsey's methodology cannot efficiently deliver: independent, unbiased consumer sentiment at deal-relevant speed.
Third, and most importantly, it establishes a precedent. Once one credible PE firm incorporates synthetic research into its diligence process, the competitive dynamics of the industry ensure that others must at least evaluate doing the same. No investment committee wants to be the one that approved a deal without the consumer data that a competitor's committee routinely receives.
Objections and Honest Limitations
The case for synthetic research in due diligence is strong but not absolute. Several legitimate objections deserve direct engagement.
"Synthetic consumers are not real consumers"
This is true by definition and beside the point. The question is not whether synthetic respondents are identical to real ones, but whether they provide information that improves decision-making relative to the alternative. The alternative, as established, is a biased sample of company-selected references supplemented by generalist consultants working under time pressure. A synthetic panel independently constructed, questioned without company involvement, and validated against traditional research at 85-92% accuracy (depending on platform and methodology) adds signal to a process that is currently noise-rich and signal-poor.
"The sample size is too small"
A typical synthetic diligence study uses 10 to 12 curated participants. This is smaller than a traditional survey but comparable to, or larger than, the number of reference calls conducted in standard diligence (typically 5 to 10). More importantly, each synthetic participant provides richer qualitative data than a 30-minute reference call, because the questioning is structured, consistent, and unaffected by the social dynamics that make reference calls politely uninformative.
"PE firms are conservative and will not adopt new methods"
This objection is empirically weakening. The adoption of synthetic research by firms like Garnett Station, combined with the broader acceptance of AI-assisted analysis in financial services, suggests that the conservatism of the industry is a speed bump rather than a roadblock. PE firms are ultimately pragmatists. They adopt tools that improve returns and abandon tools that do not. If synthetic research demonstrably reduces the frequency of deals that underperform due to market misreads, adoption will follow. The only question is speed.
"This threatens the consultancy business model"
It does. Not entirely, and not immediately, but the $50,000 to $200,000 commercial diligence engagement is premised on the assumption that independent consumer research is expensive and slow. When that assumption is no longer true, the value proposition of the traditional engagement changes. Strategy consultancies will adapt, likely by incorporating synthetic research into their own offerings. Some already are. But the standalone commercial diligence report, delivered eight weeks late and based on company-curated references, is a product whose best days are behind it.
What This Means for the Market
The integration of synthetic research into private equity due diligence is not a revolution. It is a correction. The existing process has known, structural deficiencies that persist because of practical constraints rather than methodological conviction. No serious diligence professional would argue that company-selected references constitute an unbiased sample. They use them because, until now, the alternative was worse: no consumer data at all within the deal timeline.
Synthetic research removes the constraint. It does not replace financial modelling, legal review, or operational assessment. It does not eliminate the need for expert judgment or industry experience. What it does is fill a specific gap in the information architecture of investment decisions: independent consumer sentiment, delivered at deal speed, without the target company's thumb on the scale.
For PE firms, the practical implication is that commercial diligence can now include a layer of independent consumer validation that was previously either unaffordable, too slow, or both. For target companies, the implication is that the curated reference list will carry less weight when the buyer has independent data against which to compare it. For strategy consultancies, the implication is that the value of their diligence product must shift from data collection, which synthetic research commoditises, towards interpretation and strategic recommendation, which it does not.
For the synthetic research platforms themselves, including FishDog, the PE and VC use case represents a particularly compelling market. Investment professionals are sophisticated buyers who evaluate tools on information value rather than brand familiarity. They have clear decision criteria, short evaluation cycles, and a direct line between better data and better outcomes. And the stakes involved mean that even a marginal improvement in diligence quality translates to material financial impact.
Whether this constitutes a tipping point or merely the beginning of a gradual adoption curve depends on factors that are difficult to predict from the current vantage point. What can be said with reasonable confidence is that the structural case is sound, the early adopters are credible, and the alternative it replaces was never particularly good to begin with.
Disclosure: The author is co-founder of FishDog, which offers synthetic research tools for due diligence and competes in the market described in this article. Readers should weigh the analysis accordingly.
Phillip Gales is co-founder of [FishDog](https://fish.dog). He writes about synthetic research, market intelligence, and the occasionally absurd intersection of artificial intelligence and human decision-making.