The best platforms for commercial due diligence in 2026 span three distinct technology layers: virtual data rooms for document management, expert networks for industry expertise, and customer evidence platforms for independent customer interviews. Most deal teams have the first two layers well-covered. The third — platforms that independently interview the actual customers of an acquisition target — remains the largest gap in most CDD tech stacks, and the one with the highest ROI for investment decisions.
Commercial due diligence has historically relied on a patchwork of tools optimized for different phases of the deal process. Financial and legal document review lives in virtual data rooms. Industry context comes from expert network calls. But the customer evidence layer — the part that tells you whether customers actually plan to renew, what competitors they are evaluating, and whether the growth thesis holds up under scrutiny — has been left to management-curated reference calls and expensive consulting engagements that arrive after the deal has already closed.
That gap is closing. A new category of CDD technology has emerged that interviews 50-200 customers independently, without the target company’s involvement, in 48-72 hours. This guide evaluates platforms across all three layers so deal teams can build a complete tech stack that matches the speed of competitive deal processes.
For the complete PE customer research framework — from pre-LOI validation through portfolio monitoring and exit preparation — see the complete guide to customer research for private equity.
Why Most CDD Platform Roundups Miss the Point?
Search for “best commercial due diligence platforms” and you will find list after list of virtual data rooms. Datasite, Intralinks, DealRoom, Ansarada — all excellent tools for managing the document review process. Some roundups include expert networks. Almost none address the customer evidence layer.
This matters because the questions that make or break a deal — Will customers renew? Is the competitive moat real? Can the company raise prices without churn? — cannot be answered by financial documents or industry expert opinions alone. They require direct evidence from the people who actually buy, use, and renew with the target company.
A deal team that builds its CDD tech stack around document review and expert networks alone is making investment decisions with two-thirds of the available intelligence. The missing third — independent customer evidence — is where the highest-signal data lives and where the most consequential surprises hide.
Layer 1: Document Review and Virtual Data Rooms
Virtual data rooms are the foundation of any CDD process. They manage the secure sharing and review of financial statements, legal agreements, customer contracts, IP documentation, and operational data. This category is mature, well-understood, and thoroughly covered by existing buyer’s guides. A brief overview of the leading options:
Datasite (Formerly Merrill)
Datasite is the enterprise standard for virtual data rooms in M&A transactions. Originally Merrill Corporation’s data room business, it has evolved into a comprehensive deal management platform with AI-powered document indexing, advanced permission controls, and analytics that track which documents reviewers spend the most time on. Datasite is the default choice for large-cap transactions and is used by most of the top investment banks.
Strengths: Deep institutional trust, robust security certifications, extensive analytics on reviewer behavior, global support infrastructure.
Considerations: Premium pricing reflects its enterprise positioning. Smaller deal teams may find the feature set more than they need.
DealRoom
DealRoom positions itself as the mid-market alternative to Datasite, combining virtual data room functionality with built-in project management for the diligence process. Its differentiator is workflow integration — rather than managing documents in one tool and diligence tasks in another, DealRoom attempts to unify both.
Strengths: Integrated project management, competitive pricing for mid-market deals, modern interface, good collaboration features.
Considerations: Less established in large-cap transactions. Smaller ecosystem of trained users compared to Datasite.
Intralinks
Intralinks is another enterprise-grade virtual data room with a long history in M&A. Acquired by SS&C Technologies, it benefits from integration with SS&C’s broader financial services technology stack. Strong in cross-border transactions and complex multi-party deals.
Strengths: Cross-border deal expertise, multi-language support, strong regulatory compliance, established reputation with large PE firms.
Considerations: Interface can feel dated compared to newer competitors. Pricing is on the premium end.
Ansarada
Ansarada differentiates through AI-powered deal management, including automated document organization, predictive analytics on deal outcomes, and workflow automation. Its Australian origins give it particular strength in Asia-Pacific transactions, though it has expanded globally.
Strengths: AI-powered organization and insights, strong deal analytics, modern platform design, growing global footprint.
Considerations: Smaller market share than Datasite or Intralinks in North American and European transactions.
The Limitation of Document Review Tools
Virtual data rooms are essential infrastructure, but they answer a narrow set of questions. They tell you what the company’s financials look like, what contracts exist, and what legal risks are documented. They do not tell you whether customers are happy, whether the competitive moat is real, or whether the growth thesis will survive contact with the actual market. Document review is necessary but not sufficient for commercial due diligence — it is one layer of a three-layer stack.
Layer 2: Expert Networks
Expert networks connect deal teams with industry professionals who can provide context, opinions, and expertise on markets, competitors, regulations, and operational dynamics. They are the second layer of the CDD tech stack and serve a different purpose than document review: where VDRs provide data, expert networks provide interpretation and industry context.
GLG (Gerson Lehrman Group)
GLG is the largest expert network in the world, with over one million experts across virtually every industry and geography. Founded in 1998, it pioneered the expert network model and remains the default choice for most large PE firms and consulting practices.
GLG’s scale is its primary advantage. Whatever the sector, geography, or specialization, GLG can typically connect you with multiple qualified experts within days. Their compliance infrastructure is also extensive — critical for PE firms navigating material non-public information (MNPI) concerns.
Typical cost: $1,000-$2,000 per hour-long consultation. Project-based engagements for diligence can run $50,000-$200,000 depending on scope.
Strengths: Unmatched network scale, deep compliance infrastructure, global coverage, strong in niche and specialized domains.
Best for: Market sizing, regulatory landscape analysis, competitive dynamics assessment, operational diligence questions, management team evaluation through industry peer perspectives.
Limitation for CDD: GLG connects you with industry experts who provide informed opinions. These experts are not the target company’s customers. An expert can tell you what they think about the market or the target’s competitive position. They cannot tell you whether actual customers plan to renew, what alternatives they are evaluating, or how they perceive the target’s pricing relative to value delivered. Expert opinion and customer evidence are complementary but distinct data sources.
Guidepoint
Guidepoint has grown rapidly as a GLG alternative, particularly strong in healthcare, technology, and financial services verticals. Founded in 2003, it has built a reputation for responsive service and competitive pricing compared to GLG’s premium positioning.
Guidepoint’s differentiation lies in its sector-specific depth and service model. Deal teams working in healthcare or technology often find that Guidepoint’s curated expert pool in those verticals matches or exceeds GLG’s for their specific needs, sometimes with faster turnaround on expert matching.
Typical cost: $800-$1,500 per consultation. Competitive with GLG, sometimes lower for recurring relationships.
Strengths: Strong healthcare and technology vertical depth, responsive service, competitive pricing, growing international presence.
Best for: Healthcare diligence (regulatory, reimbursement, clinical), technology market assessment, competitive landscape in specific verticals.
Limitation for CDD: Same fundamental constraint as all expert networks — you are getting expert opinions on the market, not direct evidence from the target’s customers.
Third Bridge
Third Bridge has carved out a distinct position through its Forum product — curated, multi-expert discussions on specific topics that produce pre-built interview transcripts. Rather than scheduling individual one-on-one calls, deal teams can access synthesized perspectives from panels of experts who have discussed a topic in a structured format.
This model offers efficiency advantages when you need broad market context quickly. Instead of conducting ten separate expert calls to understand a market, you can review a Forum transcript that captures perspectives from multiple experts in a single, structured document.
Typical cost: $800-$1,500 per interaction. Forum access and subscription models vary.
Strengths: Forum model provides efficient multi-expert synthesis, curated and structured transcripts, strong content library for recurring topics, growing primary research capabilities.
Best for: Rapid market context when deal timelines are tight, understanding industry consensus on key dynamics, building foundational knowledge before deeper diligence.
Limitation for CDD: Forums are valuable for market context but remain expert opinion, not customer evidence. The curated nature of Forums also means topics are selected for broad relevance, not tailored to a specific deal thesis.
Tegus
Tegus has disrupted the expert network market with a platform-first approach. Its core differentiator is a searchable library of expert call transcripts — rather than starting from scratch with every engagement, deal teams can search across thousands of existing transcripts to find relevant insights before commissioning new calls.
This model dramatically reduces the per-insight cost for preliminary research. A team evaluating a new sector can review dozens of relevant transcripts before spending anything on live expert calls, accelerating the initial learning curve.
Typical cost: Subscription-based platform access plus per-call fees for new expert consultations. Lower per-insight cost than traditional expert networks for teams that leverage the transcript library heavily.
Strengths: Searchable transcript library accelerates preliminary research, platform model reduces per-insight cost, strong in investment research, growing expert network for custom calls.
Best for: Preliminary sector research, building investment theses before deep diligence, finding patterns across multiple expert perspectives, reducing ramp time on unfamiliar industries.
Limitation for CDD: The transcript library is a powerful starting point, but transcripts from general expert calls may not address the specific questions relevant to your deal thesis. Custom calls share the same expert-opinion-not-customer-evidence limitation as other networks.
AlphaSights
AlphaSights rounds out the major expert networks with a focus on speed and service quality. Known for fast expert matching and strong client service, AlphaSights has built particular strength with financial services firms and PE sponsors who value responsiveness and reliability.
Typical cost: Premium pricing comparable to GLG. $1,000-$2,000 per consultation.
Strengths: Fast expert matching (often same-day for common sectors), strong service culture, deep relationships with financial services clients, reliable quality.
Best for: Time-sensitive diligence where speed of expert access matters, financial services sector expertise, repeat engagement models with consistent quality requirements.
Limitation for CDD: Same as other expert networks. Speed and service quality are excellent, but the data source remains industry experts rather than the target’s actual customers.
The Expert Network Ceiling
Expert networks are genuinely valuable for CDD. They provide context that cannot be found in documents — market dynamics, regulatory trends, competitive positioning through the eyes of industry participants, and operational benchmarks. No responsible diligence process should skip this layer.
But expert networks have a structural ceiling when it comes to the customer evidence questions that often determine deal outcomes. An industry expert can tell you that a market is competitive and that the target has a strong reputation. They cannot tell you that 23% of the target’s enterprise customers are actively evaluating alternatives, or that the most-cited reason for considering alternatives is response time degradation after the target’s last acquisition — the kind of specific, quantified, behavioral evidence that changes investment committee decisions.
This is not a criticism of expert networks. It is a recognition that they answer different questions than customer evidence platforms, and both are needed.
Layer 3: Customer Evidence Platforms — The Gap in Most CDD Tech Stacks
This is where most deal team technology stacks have a blind spot. The first two layers — document review and expert networks — are well-established categories with mature providers. The third layer — platforms that independently interview the actual customers of an acquisition target — is nascent, underserved, and arguably the most consequential for investment decisions.
The customer evidence layer answers the questions that financial documents and expert opinions cannot:
- Retention risk: Are customers planning to renew? What would trigger a switch?
- Growth thesis validation: Is the expansion revenue story real? Are customers buying more or contracting?
- Competitive positioning: How do customers perceive the target versus alternatives? Is the moat real?
- Pricing power: Can the company raise prices? What is the perceived value-to-price ratio?
- NPS and satisfaction: What do customers actually think — not what the management deck says they think?
- Post-acquisition risk: Will customers stay through an ownership transition?
These questions require talking to the people who write the checks, use the product, and make renewal decisions. Not industry experts. Not management-selected references. Actual customers, recruited independently, interviewed with structured methodology.
User Intuition
User Intuition is an AI-moderated customer interview platform designed for speed, scale, and independence — the three qualities most critical for deal team timelines. The platform interviews 50-200 customers of an acquisition target in 48-72 hours, recruited independently from a 4M+ panel without the target company’s involvement.
The methodology centers on conversational AI that conducts 30+ minute interviews with 5-7 levels of laddering depth. This is not a survey with branching logic. It is an adaptive conversation that follows the participant’s responses into the specific dynamics of their relationship with the target company — probing beyond surface satisfaction into the behavioral evidence that predicts retention, expansion, and churn.
Cost: $20 per interview. A typical CDD study of 100 customers runs $2,000-$15,000 depending on scope, compared to $100,000-$500,000 for traditional consulting firm customer research.
Turnaround: 48-72 hours from launch to synthesized findings. Studies can be designed and fielded within a day of engagement.
Sample size: 50-200 customers per study, with the ability to segment by customer type, tenure, geography, product line, or any other dimension relevant to the deal thesis.
Independence: Customers are recruited from a 4M+ panel and public customer databases. The target company has no knowledge of the study, no involvement in participant selection, and no opportunity to coach responses. This independence is what separates customer evidence from reference calls.
Deliverables: IC-memo-ready reports with quantified findings, verbatim quotes, segment-level analysis, and specific risk callouts. Designed for investment committee consumption, not academic research decks.
Compliance: ISO 27001 certified, GDPR compliant, HIPAA ready. Data handling meets the security requirements of institutional investors.
Strengths: Speed that fits competitive deal timelines, sample sizes large enough for statistical confidence, complete independence from the target, cost structure that allows multiple studies per deal, depth methodology that surfaces behavioral evidence rather than satisfaction platitudes.
Best for: Customer retention risk assessment, growth thesis validation, competitive positioning analysis, NPS and satisfaction benchmarking, pricing power evaluation, post-acquisition integration risk.
For a deeper look at how AI moderation changes commercial due diligence specifically, see AI commercial due diligence.
Traditional Consulting Firms
The incumbent approach to customer evidence in CDD is hiring a consulting firm — Bain, LEK, McKinsey, or a specialized diligence shop — to conduct customer reference calls as part of a broader commercial diligence engagement.
These firms bring genuine expertise. Their teams understand deal dynamics, know how to structure findings for investment committees, and can contextualize customer feedback within broader market analysis. The brand credibility of a top-tier firm also carries weight in IC presentations.
Cost: $100,000-$500,000 for a commercial diligence engagement that includes customer calls. The customer interview component is typically a subset of a broader scope that includes market sizing, competitive analysis, and growth modeling.
Turnaround: 6-12 weeks for a full commercial diligence engagement. The customer call component alone typically takes 3-6 weeks due to scheduling logistics.
Sample size: 5-15 customer calls, often limited by budget and timeline constraints. Some firms supplement with broader surveys, but the core evidence comes from a small number of in-depth calls.
Independence: This is where the consulting model faces its most significant structural challenge. In most engagements, the target company provides the customer reference list. The consulting firm may push for a broader list, but the target has effective veto power over who gets called. The result is the same selection bias that plagues all reference call approaches — management surfaces their happiest customers.
Strengths: Brand credibility in IC presentations, ability to contextualize customer feedback within broader market analysis, experienced deal team professionals, comprehensive commercial diligence scope beyond just customer evidence.
Considerations: Timelines that often exceed competitive deal processes, sample sizes too small for reliable pattern recognition, dependence on target-provided reference lists, cost that limits the number of studies per deal, findings that frequently arrive after the go/no-go decision has already been made.
The Category Is Nascent
It is worth noting that the customer evidence platform category for CDD is still emerging. Most PE firms today rely on one of two approaches: consulting firm reference calls (expensive, slow, small samples, management-curated) or skipping structured customer evidence entirely and relying on expert network calls plus the target’s own customer metrics.
The gap between what is available and what is practiced represents a significant opportunity for deal teams willing to adopt new methodology. The cost and speed economics of AI-moderated customer interviews make it practical to run customer evidence on every deal, not just the largest ones — fundamentally changing the risk profile of the diligence process.
Comparison: Expert Networks vs. Customer Evidence Platforms vs. Consulting Firms
The three approaches to gathering human intelligence in CDD serve different purposes and operate with different constraints. Understanding these differences prevents the common mistake of treating them as interchangeable.
| Dimension | Expert Networks | AI Customer Interviews | Consulting Firms |
|---|---|---|---|
| Data source | Industry experts and former executives | Actual customers of the target | Management-selected customer references |
| Typical cost | $50,000-$200,000 per deal | $2,000-$15,000 per study | $100,000-$500,000 per engagement |
| Turnaround | 2-4 weeks | 48-72 hours | 6-12 weeks |
| Sample size | 10-20 expert calls | 50-200 customer interviews | 5-15 reference calls |
| Independence | Experts selected by network based on fit | Customers recruited independently, target uninvolved | Customers typically selected by target |
| Question types | Market sizing, regulatory, competitive landscape | Retention, satisfaction, pricing power, churn risk | Mix of market context and customer satisfaction |
| Depth | Variable, depends on expert and interviewer | 5-7 level laddering, 30+ minute conversations | Variable, depends on consultant and access |
| Deal timeline fit | Moderate — scheduling can delay | Strong — 48-72 hours fits any process | Weak — 6-12 weeks often misses the decision |
The key insight is that these three approaches answer fundamentally different questions:
Expert networks answer: What does the market look like? What are the competitive dynamics? What regulatory changes are coming? How does this company compare to its peers?
AI customer interview platforms answer: Do customers plan to renew? What would make them switch? How do they perceive the target versus alternatives? Is the growth story real?
Consulting firms answer: What is our overall commercial assessment? What do the financial projections imply? What do selected customers say about their experience?
A deal team that uses only one of these approaches is working with an incomplete picture. The strongest CDD processes layer all three.
How Do You Build Your CDD Tech Stack?
Building an effective commercial due diligence technology stack is not about choosing one platform. It is about layering complementary tools that cover all three intelligence needs: document evidence, industry context, and customer truth.
The Foundation: Always Start With Document Review
Every deal needs a virtual data room. This is table stakes. Choose based on deal size, geographic complexity, and existing institutional relationships:
- Large-cap, complex transactions: Datasite or Intralinks
- Mid-market deals with workflow needs: DealRoom
- Asia-Pacific focus or AI-forward preference: Ansarada
The VDR decision is rarely the one that determines deal outcomes. Pick a solid platform and move to the layers that generate differentiated insight.
The Context Layer: Expert Networks for Industry Intelligence
Layer in expert network calls for the questions that require industry expertise rather than customer data:
- Market sizing and TAM validation — experts who have studied the market can pressure-test the target’s market size claims
- Regulatory risk assessment — former regulators and compliance professionals understand the landscape better than any document review
- Competitive dynamics — industry veterans can map the competitive landscape and identify threats that are not yet visible in market share data
- Operational benchmarking — former operators can assess whether the target’s margins, growth rates, and operational metrics are realistic
- Management quality signals — industry peers can provide perspective on the target’s leadership team
For most deals, 10-15 expert calls across these dimensions provides sufficient industry context. Tegus transcript library can accelerate the preliminary phase, followed by custom calls through GLG, Guidepoint, or AlphaSights for deal-specific questions.
The Evidence Layer: Customer Interviews for Investment Thesis Validation
The customer evidence layer is where the highest-signal diligence data lives. This is where you learn whether the story in the management presentation matches the reality experienced by the people who pay the target company every month.
Add customer evidence for every deal where the thesis depends on:
- Customer retention — if the model assumes 90%+ gross retention, verify it with independent customer interviews
- Growth through expansion — if the thesis assumes net revenue retention above 110%, confirm that customers are actually buying more
- Competitive moat — if the valuation premium reflects competitive advantage, test whether customers perceive that advantage
- Pricing power — if the model assumes price increases, determine whether customers will absorb or defect
- Post-acquisition stability — if the hold-period plan involves operational changes, assess whether customers will stay through the transition
The economics of AI-moderated customer interviews make this practical on every deal, not just the largest ones. At $2,000-$15,000 per study with 48-72 hour turnaround, customer evidence can be a standard part of the diligence process rather than an occasional luxury.
The Three Layers Working Together
Consider a concrete example. A mid-market PE firm is evaluating a $200M SaaS acquisition. The management deck claims 95% gross retention, 120% net revenue retention, and dominant competitive positioning in its vertical.
Document review (VDR): Confirms the financial statements support the retention claims at an aggregate level. Contracts show multi-year terms with annual escalators. No obvious red flags.
Expert network calls (10-12 calls): Industry experts confirm the market is growing and the target is well-regarded. Two experts note that a well-funded competitor is gaining traction in the enterprise segment. A former employee provides context on recent organizational changes. Market sizing analysis supports the TAM claims within a reasonable range.
Customer evidence (100 independent interviews): Reveals that while aggregate retention is 95%, enterprise customers (who represent 60% of revenue) show significantly lower satisfaction than SMB customers. Twenty-three percent of enterprise customers have evaluated the well-funded competitor identified by expert calls. The most-cited concern is product development velocity — customers feel the target is shipping features for the SMB segment at the expense of enterprise needs. Net revenue retention among the top 20% of accounts is 105%, not 120%, once you exclude one anomalous upsell.
Without the customer evidence layer, the deal team sees a clean financial picture and a supportive industry assessment. With it, they see a specific, quantified risk in the enterprise segment that changes the hold-period model, the post-acquisition product roadmap, and potentially the entry valuation.
This is not a hypothetical scenario. It is the pattern that repeats across PE deals where the customer evidence layer tells a different story than the management narrative.
Evaluation Criteria for Customer Evidence Platforms
When selecting a platform for the customer evidence layer of your CDD tech stack, eight criteria matter most:
1. Turnaround Time
Does the platform deliver results within competitive deal timelines? In most PE processes, the window for diligence insight to influence the decision is measured in days, not weeks. A platform that delivers in 48-72 hours fits virtually any deal process. One that takes 6-12 weeks delivers insight too late to matter.
Benchmark: 48-72 hours from study launch to synthesized findings.
2. Sample Size
Pattern recognition requires sufficient volume. Five to ten reference calls cannot reliably identify segment-level trends, detect minority-but-significant dissatisfaction patterns, or provide statistical confidence in any finding. Fifty or more interviews is the minimum threshold for most CDD applications. One hundred or more allows meaningful segmentation by customer type, tenure, geography, or product line.
Benchmark: 50+ interviews minimum, with the ability to scale to 200+ for complex deal theses.
3. Recruitment Independence
This is the single most important quality criterion for CDD customer evidence. If the target company selects which customers are interviewed, the entire exercise is compromised by selection bias. True independence means customers are recruited from external panels or public databases, the target has no knowledge of the study, and the target has no input on participant selection.
Benchmark: 100% independent recruitment from a panel of 4M+ participants. Zero target involvement.
4. Depth Methodology
Surface-level questions produce surface-level answers. The platform should employ multi-level probing — 5-7 levels of follow-up that move past polished satisfaction language into the specific behaviors, perceptions, and intentions that predict future actions. This is the difference between “I’m satisfied with the product” and “I renewed last year because switching costs were too high, but we have already shortlisted two alternatives for the next renewal cycle.”
Benchmark: 5-7 level laddering in 30+ minute conversations, with adaptive follow-up based on participant responses.
5. Deliverable Format
Due diligence findings need to reach the investment committee in a format that supports decision-making. Academic research decks with 100 slides of methodology explanation do not serve this purpose. IC-memo-ready deliverables with quantified findings, executive summaries, risk callouts, and verbatim supporting evidence do.
Benchmark: IC-memo-ready reports with quantified findings, segment analysis, and specific risk identification.
6. Compliance Certifications
Institutional investors have strict requirements around data handling, privacy, and security. The platform should hold relevant certifications — ISO 27001 for information security management, GDPR compliance for European participants, and HIPAA readiness for healthcare-adjacent targets.
Benchmark: ISO 27001 certified, GDPR compliant, HIPAA ready, with documented data handling procedures.
7. Panel Quality and Size
The quality and size of the participant panel determines whether the platform can reliably recruit customers of the specific acquisition target. A panel of 4M+ participants provides broad coverage across industries, geographies, and company sizes. Screening processes should verify actual product usage, employment at the target’s customer companies, and relevant decision-making authority.
Benchmark: 4M+ panel with multi-layer screening for product usage verification.
8. Bias Controls
Every research methodology has potential sources of bias. The platform should have documented controls for selection bias (independent recruitment), social desirability bias (AI moderation reduces the pressure to give polished answers), recall bias (structured probing on specific experiences rather than general impressions), and sample bias (demographic and firmographic monitoring during recruitment).
Benchmark: Documented bias controls across recruitment, moderation, and analysis, with transparency on methodology limitations.
The CDD Platform Landscape Is Shifting
The commercial due diligence technology landscape in 2026 looks fundamentally different from even two years ago. Virtual data rooms continue to add AI capabilities for document analysis. Expert networks are expanding their platforms with transcript libraries and searchable databases. And a new category — AI-moderated customer evidence platforms — is filling the gap that has existed since the first PE firm realized that reference calls are not customer research.
For deal teams building or upgrading their CDD tech stack, the key decisions are:
-
Document review is a solved problem with multiple excellent options. Pick one that fits your deal size and institutional relationships.
-
Expert networks are a mature category with meaningful differentiation between providers. GLG for breadth, Guidepoint for vertical depth, Third Bridge for curated forums, Tegus for the transcript library, AlphaSights for speed. Most firms develop relationships with two or three networks and allocate based on the deal’s sector needs.
-
Customer evidence is the frontier. The cost of AI-moderated customer interviews has dropped to the point where independent customer research is practical on every deal, not just the largest. The turnaround time fits competitive processes. The sample sizes enable real pattern recognition. And the independence from the target company eliminates the selection bias that has always undermined reference calls.
The deal teams that will make the best investment decisions in 2026 are not the ones with the most expensive VDRs or the largest expert network budgets. They are the ones that have built all three layers of the CDD tech stack — and particularly the ones that have closed the customer evidence gap that their competitors are still ignoring.
Getting Started
If you are evaluating platforms for your CDD process, here is a practical starting point:
If you already have a VDR and expert network relationships: Your highest-impact next step is adding the customer evidence layer. Run a pilot study on your next deal — 100 independent customer interviews alongside your standard diligence process. Compare what you learn from customer evidence to what you learn from reference calls. The delta will make the case for permanent adoption.
If you are building a CDD process from scratch: Start with all three layers from the beginning. The total cost of a modern CDD tech stack — VDR, selective expert calls, and AI-moderated customer interviews — is lower than what many firms spend on expert networks alone.
If you are a consulting firm or diligence provider: Consider integrating AI-moderated customer interviews into your service offering. Your clients need customer evidence but are not getting it from reference calls. Offering independent customer interviews at scale differentiates your practice and strengthens the quality of your commercial assessments.
For more on how independent customer evidence changes specific aspects of the diligence process, see:
- Customer due diligence questions for PE — 50 research-grade interview questions organized by thesis type
- Commercial due diligence cost analysis — detailed cost comparison across methodologies
- AI in commercial due diligence — how AI moderation specifically improves CDD quality and speed
- Commercial due diligence solutions — how User Intuition supports PE deal teams