AI tools are changing how investors approach due diligence across private markets. In this week’s blog, learn about AI-powered due diligence and how to use LLMs, data platforms, and web-based research tools to analyze investment materials, surface insights, and evaluate companies more efficiently.

AI-Powered Due Diligence: Tools and Tactics for Private Market Investors

As private market investing continues to evolve, artificial intelligence (AI) is becoming a more practical tool in the due diligence process. For some investors, diligence starts with investment memos, often supplemented with independently sourced information. Others, particularly institutional investors and angel investors, may have access to more extensive materials such as data rooms, financial models, and internal documents.

In both cases, reviewing and interpreting this information can be time-consuming. AI tools, including large language models (LLMs), data platforms, and web-based research tools, can help streamline this process by synthesizing information, structuring analysis, and surfacing insights that may otherwise be missed. Learn more about how investors can leverage AI tools and tactics to enhance due diligence in this week’s blog.

AI-Powered Due Diligence

The due diligence process can vary depending on the level of access available to an investor. Some rely on curated materials like investment memos and others work through data rooms that often include larger datasets and supporting documentation such as financial models, detailed financial statements, customer data, contracts, and internal reports. Regardless of the starting point, the volume of information can be substantial, and AI tools, particularly LLMs, can help investors move faster by organizing, summarizing, and analyzing this information in a more structured and efficient way.

So how can investors actually use these tools in practice?

Using AI to Break Down Investment Memos

For investors working from curated materials like investment memos, AI can help sort through information and quickly pull out the key points. Instead of reading every section line by line, investors can use LLMs to summarize the material, flag strengths and risks, and surface initial questions, making it easier to focus on what matters most across product, business model, traction, financials, and team. For non-technical investors, this can also support an initial layer of technical diligence by simplifying complex product concepts, helping assess whether claims appear feasible, and highlighting areas that may warrant deeper review.

Using AI to Analyze Data Room Materials and Larger Datasets

For investors with access to more detailed materials, AI can help work through data rooms that often include larger datasets and supporting documentation such as financial models, detailed financial statements, customer data, contracts, and internal reports. Instead of reviewing each document one by one, investors can use AI to pull together information across sources, spot patterns, and flag anything that doesn’t quite line up. This can make it easier to connect insights across documents, summarize more complex data, and focus on areas that may need a closer look.

Using AI to Connect Insights and Evaluate Claims

Beyond summarization, AI can help connect information across different sections of an investment memo or data room materials. Investors can use it to check whether reported traction aligns with the business model or whether financial performance supports growth claims. It can also compare key statements against market benchmarks to flag anything that stands out.

Using AI to Identify Risks and Generate Questions

AI tools can help surface inconsistencies, missing information, or areas that are not fully explained, including potential diligence red flags. Investors can use this to identify risks and develop more targeted follow-up questions. For example, AI can highlight gaps in the company’s strategy, flag unclear metrics, or suggest areas that may require deeper review.

Using AI to Gather and Cross-Check Information

In addition to reviewing provided materials, investors can use AI alongside web search, data platforms, and simple web scraping tools to gather additional context. This may include company announcements, partnerships, product launches, and broader market coverage. These tools can also help compile background information on founders and executives. This approach can be useful for both investors with limited materials and those validating information from more detailed data room documents, helping build a more complete picture while cross-checking key claims.

Key Considerations

While AI can enhance the due diligence process, investors may also want to consider potential limitations when incorporating these tools into their workflows. The following are some key considerations to keep in mind.

Accuracy and Verification

AI outputs are only as reliable as the underlying data and models. Key findings should be verified, with conclusions supported by primary sources rather than relying solely on generated summaries.

Bias and Data Limitations

AI tools may reflect biases in their training data or miss important context. Reviewing multiple sources can help provide a more complete and balanced view.

Over-Reliance on Automation

AI can support the diligence process, but it should not replace independent judgment. Investors should still think critically when evaluating opportunities.

Final Thoughts

AI tools can serve as a useful complement to traditional due diligence, whether investors are working from high-level summaries or more detailed datasets. When used thoughtfully, these tools can help investors move faster, ask better questions, and make more informed decisions when evaluating private market opportunities.

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Want to learn more about investing in startups? Check out the following MicroVentures blogs to learn more:

• Going Public: Direct Listing vs IPO vs SPAC
• Understanding Voting vs Non-Voting Shares
• Developing Your Investment Thesis
• Learning From Failed Startups

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The information presented here is for general informational purposes only and is not intended to be, nor should it be construed or used as, comprehensive offering documentation for any security, investment, tax or legal advice, a recommendation, or an offer to sell, or a solicitation of an offer to buy, an interest, directly or indirectly, in any company. Investing in both early-stage and later-stage companies carries a high degree of risk. A loss of an investor’s entire investment is possible, and no profit may be realized. Investors should be aware that these types of investments are illiquid and should anticipate holding until an exit occurs.