Why Is Due Diligence Different for AI Startups?
AI startups raising venture capital face unique due diligence scrutiny beyond what traditional software companies encounter. Investors evaluate not just business fundamentals but also technical feasibility of AI approaches, quality and provenance of training data, intellectual property ownership and protection, regulatory compliance and liability risks, and competitive moats in rapidly evolving markets.
For founders building companies around technologies like machine learning models, computer vision systems, natural language processing, or specialized AI applications, understanding what investors will examine and how to prepare for scrutiny is critical to successful fundraising. Issues discovered during due diligence can derail deals, reduce valuations, or require extensive remediation.
Proactive preparation addressing common AI-specific diligence concerns positions startups for smoother fundraising processes, stronger valuations, and more favorable deal terms.
Technical Due Diligence Considerations
Model Performance and Validation
Investors want evidence that AI systems perform as claimed. Prepare documentation of benchmarking results against industry standards, validation on held-out test sets, performance metrics across diverse scenarios, and comparisons to state-of-the-art baselines.
Overstated performance claims discovered during diligence damage credibility and investor confidence.
Technical Architecture and Scalability
Investors assess whether AI architectures can scale commercially including computational efficiency for deployment, ability to handle increased data volumes, latency and throughput capabilities, and infrastructure cost projections.
Systems that work well in research but can’t scale economically face significant challenges.
Data Strategy and Quality
AI systems depend on data. Investors evaluate training data volume and diversity, data quality and labeling accuracy, ongoing data acquisition strategies, and data refresh and model retraining plans.
Insufficient data moats or unsustainable data strategies raise concerns about long-term competitiveness.
Intellectual Property Due Diligence
IP Ownership Verification
Investors require clear documentation that the startup owns its IP. Provide executed IP assignment agreements from all founders and employees, confirmations that no third parties have ownership claims, documentation of contractor IP assignments, and disclosure of any university or prior employer IP issues.
Missing or defective IP assignments can be deal-breakers requiring expensive remediation.
Patent Portfolio Assessment
For AI startups with patents or applications, investors examine patent quality and breadth, freedom to operate analyses, prosecution status and examiner rejections, and defensive value against competitors.
While not all AI startups need patents, those pursuing patent strategies should demonstrate quality filings.
Trade Secret Protection
Document trade secret protection measures including technical access controls for proprietary algorithms, confidentiality agreements with employees and contractors, security policies and training, and incident response for potential misappropriation.
Weak trade secret protection suggests vulnerability to competitive threats.
Open Source Compliance
Investors scrutinize open source usage. Provide comprehensive open source inventories, verification of license compliance, confirmation that GPL or AGPL code isn’t inappropriately incorporated, and documentation of permissive license compliance.
Open source violations can require significant product modifications.
Data Rights and Privacy Compliance
Training Data Rights
Investors want assurance that training data was lawfully obtained. Document sources of training data, licenses or permissions for proprietary data, compliance with data privacy laws for personal data, and representations from data providers about their rights.
Copyright or privacy issues with training data create substantial liability risks.
GDPR and Privacy Compliance
For startups processing personal data or operating in Europe, demonstrate GDPR compliance including legal bases for data processing, data processing agreements with customers, privacy policies and disclosures, and data protection impact assessments for high-risk processing.
Privacy violations or inadequate compliance programs concern investors about regulatory exposure.
Biometric and Sensitive Data Handling
If processing biometric data, health information, or other sensitive categories, show heightened compliance measures including specific consents or legal justifications, enhanced security protections, and compliance with sector-specific regulations.
Regulatory and Compliance Risks
Industry-Specific Regulations
AI applications in regulated industries face additional scrutiny. For healthcare AI, address FDA medical device classifications if applicable and HIPAA compliance. For financial services AI, show compliance with banking regulations and SEC oversight. For employment AI, demonstrate EEOC anti-discrimination compliance.
Emerging AI Regulations
Investors increasingly evaluate exposure to AI-specific regulations including the EU AI Act risk classifications and compliance roadmaps, state AI laws and restrictions, and proposed federal AI legislation.
Understanding regulatory trajectory helps investors assess long-term viability.
Export Control Compliance
For AI involving advanced computing or international operations, document export control classifications, licenses for restricted exports, and screening processes for Entity List compliance.
Export violations carry severe penalties and can restrict business operations.
Contracts and Customer Relationships
Customer Contract Review
Investors examine customer agreements for revenue sustainability, performance warranties and liability exposure, IP ownership and licensing terms, and termination rights and notice periods.
Unfavorable contract terms can indicate weak negotiating position or excessive risk.
Partnership and Vendor Agreements
Review material partnerships including data provider agreements, infrastructure and cloud services contracts, technology licensing arrangements, and strategic partnership terms.
Dependencies on specific vendors or partners may create business risks.
Revenue Concentration
High customer concentration increases risk. Disclose percentage of revenue from top customers, contract terms with major customers, and strategies for diversifying customer base.
Team and Talent Due Diligence
Key Person Analysis
Investors evaluate whether the team can execute the vision including technical expertise in relevant AI domains, prior startup or commercialization experience, complementary skills across engineering and business, and depth beyond just founders.
Over-reliance on individual founders creates key person risks.
Employment Agreements and Restrictive Covenants
Provide employment agreements with key employees showing IP assignment provisions, confidentiality obligations, and where enforceable, non-compete or non-solicitation clauses.
Strong employment protections reduce risks of talent or IP loss.
Immigration and Work Authorization
For international team members, confirm work authorization status including visa types and expiration dates, green card application status, and contingency plans if authorization issues arise.
Financial and Business Model Diligence
Unit Economics
AI business models must demonstrate sustainable economics including customer acquisition costs, lifetime value projections, gross margins after infrastructure costs, and path to profitability.
High compute costs for inference or expensive data acquisition can undermine unit economics.
Burn Rate and Runway
Provide detailed financial projections showing monthly burn rates, expected runway from current and new funding, key milestones achievable within runway, and trigger points requiring additional capital.
Competitive Analysis
Investors assess competitive positioning. Document direct and indirect competitors, comparative advantages and differentiation, barriers to entry and competitive moats, and market positioning and go-to-market strategy.
Litigation and Disputes
Disclosure of Material Disputes
Disclose all material litigation, disputes, or claims including active lawsuits or arbitrations, threatened claims or demand letters, regulatory investigations or inquiries, and IP disputes or cease and desist notices.
Undisclosed litigation discovered during diligence severely damages trust.
Resolution Status and Reserves
For known disputes, provide current status and likelihood of outcomes, settlement negotiations or resolution efforts, and financial reserves or insurance coverage.
Corporate Governance and Cap Table
Corporate Structure and Documentation
Provide clean corporate records including articles of incorporation and bylaws, board and stockholder meeting minutes, stock ledgers and option grants, and prior financing documents.
Incomplete or inconsistent records suggest governance weaknesses.
Capitalization Table Clarity
Present clear capitalization tables showing all equity holders and ownership percentages, outstanding options and warrant coverage, liquidation preferences and conversion terms, and anti-dilution protections.
Complex or unclear cap tables complicate future financings.
Founder Vesting and Equity
Demonstrate founder equity is subject to vesting preventing founders from leaving with full equity, reverse vesting schedules typical of early-stage companies, and acceleration provisions for specific events.
Preparing the Data Room
Organizing Due Diligence Materials
Create well-organized virtual data rooms with logical folder structures including corporate documents and governance, intellectual property materials, contracts and commercial agreements, technical documentation and data, financial statements and projections, and compliance and regulatory documentation.
Redaction and Confidentiality
Redact sensitive information in shared documents like trade secrets in technical materials, customer names in sensitive contexts, and personal information requiring privacy protection, while maintaining document usefulness.
Continuous Updates
Keep data rooms current throughout fundraising including updating materials as circumstances change, adding newly executed agreements, and correcting errors or omissions promptly.
Managing the Diligence Process
Diligence Request Lists
Expect comprehensive diligence request lists covering all business aspects. Respond systematically and completely, note any requested items that don’t exist or apply, and proactively provide related materials.
Q&A Management
Track investor questions and responses in organized Q&A logs. Ensure consistency across responses to different investors and escalate complex questions to appropriate experts.
Issue Resolution
When diligence uncovers issues, address them transparently by acknowledging problems rather than minimizing them, proposing remediation plans with timelines, and demonstrating good faith efforts to resolve issues.
Investor trust depends on honest handling of discovered problems.
Post-Diligence Considerations
Reps and Warranties in Investment Documents
Investment agreements include extensive representations and warranties based on diligence findings. Review these carefully ensuring accuracy and completeness, negotiating appropriate qualifications or exceptions, and understanding personal liability for founder reps.
Disclosure Schedules
Prepare detailed disclosure schedules identifying exceptions to representations including known IP disputes, material contracts with unusual terms, regulatory compliance issues, and pending litigation.
Disclosure schedules protect against later breach claims.
Conclusion: Preparation Drives Successful Fundraising
AI startup fundraising requires meticulous preparation addressing technical, legal, regulatory, and business dimensions. Investors conduct thorough due diligence examining IP ownership, data rights, privacy compliance, technical feasibility, and competitive positioning.
Startups that proactively organize diligence materials, address potential issues before they’re discovered, and demonstrate strong governance and compliance position themselves for successful fundraising at attractive valuations.
Contact Rock LAW PLLC for AI Fundraising Counsel
At Rock LAW PLLC, we help AI startups prepare for fundraising and navigate investor due diligence.
We assist with:
- Due diligence preparation and data room organization
- IP ownership verification and remediation
- Privacy and regulatory compliance assessment
- Contract review and negotiation
- Investment agreement negotiation
- Corporate governance and documentation
Contact us to prepare your AI startup for successful venture capital fundraising.
Related Articles:
- Structuring Founder IP Assignments
- Preparing for M&A Due Diligence
- Employment Agreements for AI Companies
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