The AI Due Diligence Checklist for Emerging Fund Managers

By Yuri Kruman | GP, 92 Percent Fund I | Builder of DueDrill | 3x CHRO | JD, Cardozo Law

Meta Title: AI Due Diligence Checklist for VC Fund Managers (16 Categories) | 2026 *Meta Description: Complete 16-category due diligence checklist for emerging fund managers. 214 data fields. Free AI-powered DD tool included. Target Keywords: vc due diligence checklist, startup due diligence, ai due diligence tool, emerging manager due diligence*

What Is a VC Due Diligence Checklist and Why Do Emerging Managers Need One?

A VC due diligence checklist is a structured framework covering every dimension of a startup investment decision: team, market, product, financials, legal, competitive positioning and more. Emerging fund managers need one because they lack the institutional muscle, the analyst bench and the pattern recognition library that established firms rely on. Without a systematic checklist, you will miss things. You will miss expensive things.

I know because I built one from scratch.

When I launched 92 Percent Fund I as a Wyoming LP, I was simultaneously filing Form D with the SEC, managing Colorado EFD compliance and evaluating my first deals. There was no team of associates running models for me. There was no institutional DD playbook handed down from a senior partner. There was me, a legal pad and a clock running out on every term sheet.

That experience is exactly why I built DueDrill: an AI-powered due diligence tool that automates the 16-category, 214-field checklist you are about to read. But before I tell you about the tool, let me give you the checklist itself. Complete. No paywalls. No gating. Because emerging managers deserve better resources than what the industry currently provides.

How Many Categories Does a Thorough Startup Due Diligence Cover?

Sixteen. Not five. Not eight. Sixteen distinct categories, each with specific sub-items that experienced investors evaluate before writing a check. Most emerging managers cover six or seven of these categories and call it thorough. That is how you end up with portfolio companies that implode from risks you never assessed.

Here is the complete checklist.

The Complete 16-Category Due Diligence Checklist

Category 1: Team & Founders

The team is not just the first thing you evaluate. It is the thing that determines whether every other category matters. A brilliant product with a weak team will fail. A mediocre product with an exceptional team will pivot into something that works.

• Prior exits: Have the founders built and sold companies before? What were the outcomes for investors?
• Domain expertise: How deep is their knowledge of the industry they are targeting? Years of experience, not just enthusiasm.
• Educational background: Not as a prestige filter but as a signal of intellectual rigor and network access.
• Founder tenure together: How long have the co-founders worked together? Pre-existing relationships survive stress better than partnerships formed at a hackathon.
• Advisory board quality: Who advises them? Are these real advisors with skin in the game or LinkedIn decorations?
• Team gaps: Where is the team weak? Do they know it? What is the hiring plan to fill those gaps?
• Founder-market fit: Is there a credible reason these specific people are building this specific product?
• Reference checks: Talk to former colleagues, employees and investors. Not the references they provide. The ones they don't.
• Equity split and vesting: Is the cap table healthy? Are founders vested on reasonable schedules? Is there a cliff?
• Coachability: Will they listen when things go wrong, or will ego override data?

Category 2: Market Opportunity

A great team in a small market produces a small outcome. Market size is not a vanity metric. It is the ceiling on your return.

• Total Addressable Market (TAM): What is the broadest possible market for this product?
• Serviceable Addressable Market (SAM): What portion of TAM can they realistically reach with their current go-to-market?
• Serviceable Obtainable Market (SOM): What share can they capture in 3-5 years with current resources?
• Market growth rate: Is the market expanding, stable or contracting? What are the secular trends?
• Timing: Why now? What has changed in technology, regulation or buyer behavior that makes this the right moment?
• Regulatory environment: Are there regulatory tailwinds or headwinds? How could regulation change?
• Market concentration: Is the market fragmented (opportunity for consolidation) or dominated by incumbents (harder entry)?
• Geographic considerations: Is this a US-only play? Global? What are the expansion dynamics?

Category 3: Product & Technology

You are investing in what they have built and what they can build next. Technical diligence is not optional, even if you are not technical yourself. Bring in someone who is.

• Technology stack: What are they built on? Is it modern, maintainable and scalable?
• Technical moat: Is there proprietary technology that competitors cannot easily replicate?
• Patents and IP: Do they hold patents? Are they filed, pending or granted? In which jurisdictions?
• Product-market fit signals: Are users actively choosing this product over alternatives? What is the evidence?
• Scalability architecture: Can the tech handle 10x or 100x current load without a rewrite?
• Technical debt: How much legacy code or architectural shortcuts exist? What is the cleanup cost?
• Development velocity: How fast do they ship? What is the release cadence?
• Security posture: SOC 2? Pen testing? Data encryption standards? Incident response plan?
• AI/ML components: If they claim AI, is it real ML or just if-then rules with a chatbot interface?
• Open source dependencies: What is the risk profile of their dependency tree?

Category 4: Business Model

Revenue is not a business model. A business model is the entire system by which a company creates, delivers and captures value. Understand all three parts.

• Revenue model: SaaS? Marketplace? Transactional? Usage-based? Hybrid?
• Unit economics: What does it cost to acquire and serve one customer? What is that customer worth over time?
• Pricing power: Can they raise prices without losing customers? What is the elasticity?
• Gross margins: What are current gross margins? What is the path to 70%+ for software?
• Expansion revenue potential: Can existing customers buy more over time? Upsell, cross-sell, seat expansion?
• Revenue predictability: How much revenue is recurring vs. one-time? What is the contract structure?
• Path to profitability: When does the model turn cash-flow positive? What assumptions does that require?
• Business model risk: What could break this model? New competitors? Regulation? Platform dependency?

Category 5: Traction & Metrics

Traction is the antidote to storytelling. Numbers do not lie, but they can be presented selectively. Know which metrics actually matter for the stage and model.

• Annual Recurring Revenue (ARR): Current ARR and trailing 12-month trajectory.
• Revenue growth rate: Month-over-month and year-over-year. Is growth accelerating or decelerating?
• Net revenue retention (NRR): Are existing customers spending more or less over time? Best-in-class is 120%+.
• Gross churn rate: What percentage of customers leave each month/quarter/year?
• Logo retention: Separate from revenue retention. Are you keeping customers even if spend fluctuates?
• Engagement metrics: DAU/MAU ratio, session length, feature adoption. Usage predicts retention.
• Pipeline and bookings: What is in the sales pipeline? Weighted pipeline vs. closed-won ratio?
• Cohort analysis: How do customer cohorts behave over time? Are newer cohorts better or worse?

Category 6: Competitive Landscape

Every founder says they have no competitors. Every founder is wrong. If there are truly no competitors, that is either a blue ocean or a market that does not exist. Both require deeper investigation.

• Direct competitors: Who sells a similar product to the same buyer? List them. Understand their strengths.
• Indirect competitors: Who solves the same problem differently? Spreadsheets and manual processes count.
• Competitive positioning: What is the company's honest differentiation? Speed? Price? Feature depth? Integration?
• Defensibility: What prevents a well-funded competitor from replicating this in 12 months?
• Switching costs: How hard is it for a customer to leave? Data lock-in? Integration complexity? Workflow dependency?
• Competitor funding: How well-capitalized are the competitors? Are they growing faster?
• Win/loss analysis: When they lose deals, why? When they win, why? Talk to customers who chose the competitor.

Category 7: Financial Analysis

Financial models at the seed and Series A stage are fiction, but they are useful fiction. They reveal how the founders think about their business, what assumptions they make and whether those assumptions are internally consistent.

• Burn rate: Monthly cash burn. Gross burn and net burn.
• Runway: How many months of cash remain at current burn? At projected burn?
• Capital efficiency: Revenue generated per dollar raised. Is this team good stewards of capital?
• Revenue projections: 3-year and 5-year models. What are the key assumptions? Stress-test them.
• Expense breakdown: Where does the money go? R&D vs. S&M vs. G&A ratios.
• Working capital dynamics: Cash conversion cycle. Payment terms. Seasonality.
• Scenario analysis: Best case, base case and worst case. What does each scenario mean for your investment?

Category 8: Funding History

The cap table tells you more about a company's history than the pitch deck ever will. Clean cap tables and disciplined fundraising are strong signals.

• Prior rounds: How much has been raised? At what valuations? What were the terms?
• Investor quality: Who invested previously? Are they following on? If not, why not?
• Valuation trajectory: Has valuation growth been justified by traction or inflated by hype?
• Cap table cleanliness: Any dead equity? Excessive advisor shares? Complicated SAFEs or convertible notes?
• Use of proceeds: How was prior capital deployed? Did they hit the milestones they promised previous investors?
• Bridge rounds and extensions: Has the company needed bridge financing? That can signal execution issues.
• Pro rata rights and protections: What rights do existing investors have? How does that affect your position?

Category 9: Go-to-Market Strategy

The best product in the world fails without distribution. GTM is not marketing. It is the entire system of finding, converting and retaining customers.

• Primary channels: How do they acquire customers today? Outbound? Inbound? PLG? Partnerships?
• Customer Acquisition Cost (CAC): Fully loaded CAC including salaries, tools and overhead.
• LTV:CAC ratio: Lifetime value divided by acquisition cost. 3:1 or better is the benchmark.
• CAC payback period: How many months to recover the cost of acquiring a customer?
• Sales cycle length: Days from first touch to closed deal. How does this compare to competitors?
• Marketing efficiency: What is the blended cost per lead, cost per MQL and cost per SQL?
• Channel diversification: Are they dependent on a single channel? That is a risk.
• Partner and distribution strategy: Any channel partnerships? OEM deals? Integration marketplace presence?

Category 10: Customer Analysis

Customers are the ultimate validators. Talk to them. Not two. Ten. Ask hard questions. Listen for hesitation.

• Customer concentration: What percentage of revenue comes from the top 3 customers? Over 30% is a red flag.
• Net Promoter Score (NPS): What is their NPS? How do they measure and act on it?
• Customer case studies: Can they provide detailed case studies with measurable outcomes?
• Expansion revenue: Are existing customers buying more over time? What drives expansion?
• Customer segments: Which segments are growing fastest? Which have the best unit economics?
• Customer feedback themes: What do customers consistently praise? What do they consistently complain about?
• Reference customers: Talk to customers the company did not pre-select for you. Cold outreach works.

Category 11: Legal & Compliance

Legal issues are the landmines of venture investing. They sit quietly underground until someone steps on them, and then the damage is catastrophic.

• Incorporation structure: Delaware C-Corp is standard. Anything else requires a good reason.
• IP ownership: Is all intellectual property properly assigned to the company? Employee invention agreements signed?
• Litigation history: Any pending or threatened litigation? Past lawsuits and their outcomes?
• Regulatory compliance: Industry-specific regulations: HIPAA, SOX, GDPR, PCI-DSS, state licensing?
• Employment law compliance: Proper classification of employees vs. contractors? Visa and immigration compliance?
• Data privacy: Privacy policy, data handling practices, consent mechanisms, breach notification procedures?
• Material contracts: Key customer contracts, vendor agreements, partnership terms. Any problematic clauses?
• Corporate governance: Board composition, voting rights, information rights, protective provisions?

Category 12: Operational Infrastructure

Operations is the unsexy category that separates companies that scale from companies that shatter. You need to know if the plumbing works before you turn on the water.

• Hiring plan: How many hires in the next 12-18 months? In which functions? What is the budget?
• Key hires needed: Are there critical roles unfilled? VP Engineering? Head of Sales? CFO?
• Organizational structure: How is the company organized? Reporting lines? Decision-making authority?
• Operational scalability: Can current operations handle 5x growth without breaking?
• Vendor dependencies: Critical vendor relationships. Single points of failure in the supply chain?
• Internal tools and systems: CRM, HRIS, project management, communication. Are they fit for purpose?
• Culture and retention: Employee satisfaction, turnover rate, Glassdoor reviews. Culture is infrastructure.

Category 13: Risk Assessment

Every investment carries risk. The question is not whether risk exists but whether you have identified it, quantified it and determined whether the return justifies it.

• Market risk: Could the market shift, shrink or be disrupted by a new technology or regulation?
• Execution risk: Can this specific team execute this specific plan? Where are the failure points?
• Technology risk: Could the technology become obsolete? Is there platform dependency (AWS, Salesforce, Apple)?
• Regulatory risk: Could new laws or regulations damage or destroy the business model?
• Concentration risk: Over-reliance on a single customer, channel, market or team member?
• Competitive risk: Could a well-funded competitor or incumbent enter and dominate?
• Funding risk: Will this company be able to raise its next round? What happens if it cannot?
• Key person risk: What happens if the CEO leaves? Is the company dependent on one individual?

Category 14: Exit Potential

You are not investing for the journey. You are investing for the exit. If you cannot see a plausible path to a return-generating event, the investment thesis is incomplete.

• Comparable exits: What companies in this space have been acquired or gone public? At what multiples?
• Acquirer landscape: Who would buy this company? Strategic acquirers? PE firms? Larger competitors?
• Acquisition attractiveness: What makes this company a compelling acquisition target? Technology? Team? Customer base?
• IPO readiness: If the path is IPO, what revenue scale and growth rate are needed? How far away is that?
• Exit timeline: What is the realistic timeline to exit? Does that fit your fund's lifecycle?
• Return potential: At various exit scenarios, what is the multiple on invested capital? What IRR does that imply?

Category 15: ESG & Impact

This is no longer optional. LPs, especially institutional ones, increasingly require ESG reporting and impact measurement. If you ignore this category, you will lose LP commitments.

• Environmental sustainability: Carbon footprint, energy usage, sustainability commitments. Relevant for the business model?
• Diversity and inclusion: Team composition, hiring practices, board diversity. Measurable metrics.
• Social impact: Does the product or service create positive social outcomes? How is impact measured?
• Governance practices: Board independence, executive compensation, whistleblower policies, ethical guidelines.
• LP reporting requirements: What ESG data will your LPs require? Can you collect it from this portfolio company?
• Impact measurement framework: UN SDGs, IRIS+, B Corp standards. Which framework applies?

Category 16: Investment Thesis Fit

The best deal in the world is a bad deal if it does not fit your fund. This category is the most personal and the most important.

• Stage fit: Does the company's stage match your fund's investment mandate?
• Sector alignment: Is this in a sector your fund targets? Do you have domain expertise?
• Check size: Does the investment amount fit your fund's allocation strategy?
• Geographic fit: Is the company in a geography your fund covers?
• Portfolio construction: How does this deal affect your portfolio's diversification? Concentration risk?
• Follow-on capacity: Can you participate in future rounds? What reserves does your fund hold?
• Value-add potential: Can you meaningfully help this company beyond capital? Board seat, network, expertise?
• Thesis conviction: On a scale of 1-10, how strongly does this deal validate your investment thesis?

How Long Does Startup Due Diligence Take? Manual vs. AI-Assisted

Here is the reality that no one talks about at LP meetings. Due diligence takes an enormous amount of time, and emerging managers have less of it than anyone. You are fundraising, sourcing, evaluating and managing a portfolio simultaneously, often without a single analyst.

This table shows the time cost of thorough DD across all 16 categories.

That is 12-18 full working days of manual DD versus roughly 3-4 hours with AI assistance. For an emerging manager evaluating 50-100 deals per year, this is the difference between building a defensible portfolio and flying blind.

The 5 Due Diligence Mistakes Emerging Managers Make

Mistake 1: Falling in Love with the Founder Before Finishing DD

You meet a charismatic founder. The story is compelling. The vision is electric. You start cutting corners on DD because you have already decided you want in. This is how you end up with a portfolio company that has undisclosed litigation, a toxic cap table or unit economics that only work in the pitch deck.

Fix: Complete the full 16-category checklist before making any commitment, verbal or written. Every time. No exceptions.

Mistake 2: Skipping Legal and Compliance

Emerging managers, especially those without legal backgrounds, treat legal DD as a box-checking exercise. They scan the articles of incorporation, confirm it is a Delaware C-Corp and move on. Then six months later they discover the CTO never assigned his IP to the company, or there is a former co-founder with 15% equity and no vesting schedule who left on bad terms.

Fix: Legal DD is not optional and it is not cursory. Review every material contract, every employment agreement and every item on the cap table.

Mistake 3: Using Gut Feel Instead of a Systematic Framework

"I just know a good deal when I see one." No, you don't. Nobody does consistently without a framework. Pattern recognition is valuable, but it is not a substitute for systematic evaluation. The best investors in the world use checklists. You should too.

Fix: Adopt the 16-category framework. Score each category. Compare scores across deals. Let data inform intuition, not replace it.

Mistake 4: Ignoring the Competitive Landscape

Every founder minimizes competition. "We don't really have competitors" is a sentence I hear in at least 40% of pitch meetings. It is almost never true. The companies that fail most spectacularly are the ones that got blindsided by a competitor they never bothered to study.

Fix: Map direct competitors, indirect competitors and potential future entrants. Talk to customers who chose the competitor. Understand why.

Mistake 5: Not Stress-Testing the Financial Model

Founders present hockey-stick projections. Emerging managers nod along because the numbers are exciting and challenging them feels adversarial. But your job is not to be the founder's cheerleader. Your job is to protect your LPs' capital.

Fix: Run scenario analysis on every financial model. Cut revenue projections by 50%. Double the timeline. If the investment still makes sense under stress, it is worth pursuing.

What Is the Best AI Due Diligence Tool for Emerging Fund Managers?

This is where I stop being objective and start being direct: I built DueDrill because nothing else on the market did what emerging managers actually need.

Most DD tools are built for large firms with large teams. They assume you have analysts to input data, associates to run models and partners to review reports. Emerging managers do not have that infrastructure. You need a tool that takes a company name and gives you a comprehensive DD report across all 16 categories.

DueDrill automates this entire checklist in 60 seconds. It pulls data from public sources, structures it across all 16 categories, scores each dimension and produces a report you can share with your LPs. It does not replace your judgment. It gives your judgment a foundation of data instead of a foundation of slide decks.

The 214 data fields in this checklist are the same 214 fields DueDrill evaluates. I did not build the tool and then create the checklist to market it. I created the checklist through painful experience, refined it across dozens of deals and then automated it because doing it manually 50-100 times per year is not sustainable for a solo GP.

Frequently Asked Questions

How long should due diligence take for a seed-stage investment?

Even at the seed stage, thorough DD should cover all 16 categories. The depth within each category will be lighter: a seed-stage company will not have extensive financial history or customer concentration data. But the categories themselves still apply. With a systematic framework, seed-stage DD should take 2-3 weeks of calendar time. With AI-assisted tools like DueDrill, the data collection phase drops to hours, leaving you more time for the judgment calls that cannot be automated: reference checks, founder conversations and thesis alignment.

What is the difference between due diligence for emerging managers vs. established funds?

The framework is identical. The resources are not. Established funds have dedicated associates, in-house legal teams, data subscriptions and decades of pattern recognition. Emerging managers have to do the same work with a fraction of the resources and time. This is precisely why systematic checklists and AI-assisted tools matter more for emerging managers than for anyone else. The cost of a missed red flag is proportionally higher when you are managing a $10M fund vs. a $1B fund.

Should I do DD before or after issuing a term sheet?

Before. Always before. Issuing a term sheet before completing DD creates psychological commitment that biases your evaluation. It is harder to walk away from a deal after you have put terms on paper, even when DD reveals problems. Complete at least a preliminary DD pass across all 16 categories before you discuss terms. Then use the term sheet negotiation to address specific risks your DD identified.

What are the most common red flags in startup due diligence?

Five red flags that should trigger deep investigation or a pass: (1) Founders who resist providing access to data, financials or customer references. (2) Cap table problems: dead equity, excessive dilution, missing vesting schedules or unresolved co-founder disputes. (3) Customer concentration above 30% in a single account. (4) No clear answer to "why now?" that connects to a structural market shift. (5) Financial projections that assume zero competition and 100% market capture. Any one of these can be explained. Multiple red flags in the same deal is a pattern, not a coincidence.

Can AI fully replace human judgment in due diligence?

No. AI is exceptional at data collection, pattern identification, benchmarking and report generation. It can compress 100+ hours of manual research into minutes. But the final investment decision requires human judgment: assessing founder character, evaluating team dynamics, sensing market timing and making conviction-based bets on uncertain outcomes. The best approach is AI-augmented DD: let the machine handle the data layer so you can focus your limited time on the judgment calls that actually determine returns.

About the Author

Yuri Kruman is the General Partner of 92 Percent Fund I, LP, a Wyoming-based venture fund. He is a 3x Chief Human Resources Officer, holds a JD from Cardozo School of Law and a BA in Anthropology and Neuroscience from the University of Pennsylvania. Named a Top 5 Global HR Thought Leader by Thinkers360, Yuri has coached over 2,300 executives and trained AI systems for Meta, Microsoft and OpenAI. He built DueDrill (duedrill.com) to solve the DD bottleneck he experienced firsthand as an emerging fund manager. He is also the founder of BookToCourse.AI and author of "The Definitive Guide to Closing the AI Wage Gap." Based in Israel with US operations across New York, New Jersey and Washington DC.

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