Generative AI for Venture Capital Due Diligence

CMU Corporate Startup Lab Fellowship, sponsored by Bosch

Developed An Evaluation Rubric Based On Common Venture Capital Metrics

Motivation—

Due diligence plays a critical role in evaluating investment opportunities, particularly in venture capital, private equity, and corporate acquisitions. With the rise of Large Language Models (LLMs) like GPT-4 and Claude, organizations now have powerful tools to enhance the speed, accuracy, and depth of due diligence processes.

Project Overview—

I worked with Bosch (https://www.bosch-business-innovations.com/) via the CSL for ~4 months (Jan-Apr 2024) to investigate the feasibility of using state-of-the-art LLMs to increase due-diligence capabilities. This report presents my reflections during this project, exploring the potential of LLMs in due diligence, highlighting key insights, technological approaches, challenges, and applications.

To ensure a thorough assessment of corporate startup's’ business potential, we defined five key evaluation metrics.

Our Prompt Framework—

The prompt structure we outlined for evaluating startups using LLMs is inspired by the TELeR taxonomy. The TELeR taxonomy for prompt structuring is a methodical approach to creating high-performing prompts that guide LLMs to deliver precise and actionable insights. It defines a hierarchy of detail, from simple directives to complex, multi-layered prompts with role definitions, scoring rubrics, and justification guidelines. This taxonomy ensures that prompts are tailored to specific tasks, offering the right balance of flexibility and structure.

Higher-level prompts combining instructions, data, rubrics, and justification yield the most accurate, insightful LLM assessments

This structure ensures relevance and precision, enabling LLMs to deliver actionable insights efficiently. For venture capital, private equity, and corporate incubators, such high-level prompts streamline the due diligence process, reduce subjectivity, and enhance scalability by consistently evaluating market potential, technical feasibility, team strength, and business viability​​.

Ups and Downs of Using LLMs in Due Diligence—

• Ups:

  • Efficiency Gains: LLMs can process and summarize large volumes of information faster than human analysts, identifying key data points such as market size, financial health, and team credentials.

  • Consistency: Standardized evaluations using LLMs reduce the subjectivity inherent in human analysis.

  • Scalability: Tools like LangChain enable integration across diverse data sources, such as Crunchbase, Pitchbook, and proprietary databases, for seamless evaluation at scale.

  • Cost-Effectiveness: With lightweight models and ensemble methods, LLMs can deliver high-quality insights at lower operational costs compared to hiring large analysis teams.

• Downs:

  • Token and Context Limitations: Current LLMs have input/output token limits (e.g., GPT-4’s ~32k tokens), constraining the analysis of extensive documents.

  • Bias and Misinterpretation: LLMs may misinterpret poorly structured data or reinforce biases present in the input, leading to skewed insights.

  • Lack of Domain-Specific Expertise: While LLMs excel in general language understanding, they may struggle with nuanced industry-specific knowledge without sufficient fine-tuning.

Applications of LLMs in Due Diligence—

1. Market Due Diligence:

   • Validate market size and growth potential by analyzing competitor documents, market reports, and customer acquisition strategies.

   • Compare industry benchmarks and identify emerging trends through automated data collection from platforms like Gartner and Statista.

2. Business Model Assessment:

   • Evaluate scalability by simulating market scenarios and assessing financial projections for inconsistencies.

   • Identify exit strategies and investor alignment using databases like Capital IQ and Crunchbase​​.

3. Competitive Advantage Analysis:

   • Assess innovation and product differentiation through patent analysis tools and technology readiness evaluations.

   • Compare competitors' strategies to identify gaps and opportunities for differentiation​.

4. Operational Due Diligence:

   • Analyze team credentials, regulatory compliance, and partnership strategies to mitigate operational risks.

   • Leverage sentiment analysis and entity recognition to gauge team competence and alignment with business objectives​.

Considerations for Effective LLM Implementation—

• Combine LLM outputs with human expertise to validate insights and reduce the impact of errors.

• Optimize API usage to balance accuracy with expense. For instance, a single GPT-4 query costs ~$0.24 per 800-word response​.

• Use structured prompts tailored to specific tasks, such as evaluating market potential or technology readiness levels​.

• Regularly update rubrics, prompts, and datasets to reflect evolving industry standards and business needs.

Conclusion—

LLMs have the potential to revolutionize due diligence processes by making them faster, more consistent, and scalable. However, challenges like bias, token limitations, and domain-specific expertise must be addressed to unlock their full potential. Combining LLMs with complementary tools and human expertise will enable organizations to make data-driven investment decisions with greater confidence and precision.

Acknowledgement—

Extremely grateful to Corporate Startup Lab, a CMU Swartz Center Initiative, our sponsor Bosch and all the project leaders, my co-researcher, Dipam Paul. Special shout out to the CSL team for this incredible opportunity!