PE Due Diligence: A Case for deploying LLMs

The due diligence process of private equity (PE) companies requires rigorous scrutiny and assessment of numerous reports, contracts, and extensive data from "the data room" in a significantly crunched amount of time. The deal teams are exhausted, working continuous hours without sleep and often challenged due to sheer time compressed nature of the process. As we build the generative AI, this process can be more humanized by deploying customized LLM technologies which can improve the data room analytics, searchability and organization. With their ability to ingest and process vast amounts of data, LLMs can provide more direct responses to both conceptual and factual questions, thereby reducing risks and improving the detailed examination of contracts and reports in a more digestible manner.

As we continue to build better tools, this acquisition area can significantly enhance risk management for PE deals. Generative AI Large Language Models, which are still evolving, can leverage external data, word embeddings, and their capacity to analyze new information. As we deploy sophisticated engineering architectures on top of LLMs using tools like the LLAMA Index, multi-stage pipelines, PineCone, and other vector databases, the due diligence process in the PE industry is set to undergo substantial changes.

LLMs bring immense value with their ability to read, understand, and extract information from massive volumes of text - a crucial component in the due diligence process. They can summarize contracts, identify key terms or conditions, and highlight potential issues. One powerful feature of LLMs is their capability for document management, which is extremely useful in the due diligence process. They can categorize and organize documents, extract basic information, and simplify the process.

Another significant advantage of LLMs is their multi-lingual capability, which proves beneficial when analyzing data across multiple countries and languages. Here's how I believe LLMs can influence various stages of due diligence:

1. Financial Due Diligence: The in-depth exploration of financial aspects involves statements, tax returns, budgets, forecasts, and more. LLMs can interpret this vast amount of data more efficiently and focus on hidden liabilities, such as lawsuits or unknown debts, thereby reducing risks.
2. Operational Due Diligence: By processing operational data, LLMs can answer questions about anomalies, risk patterns, and improvement opportunities.
3. Legal Due Diligence: Reading all contracts, agreements, and checking compliance is often challenging and time-consuming. LLMs can effectively analyze large amounts of data and legal documents, simplifying complexities and identifying potential breaches.
4. Market and Industry Due Diligence: LLMs, with their ability to ingest substantial amounts of data, can be highly useful in analyzing market diligence data.

LLMs can also analyze the data in the "data room," facilitating a deeper dive into the data. We might soon see the emergence of LLMs or generative AI-enabled data rooms where the data room itself improves semantic searches using vector databases, making the review process more efficient.

Challenges in adoption and application mainly stem from privacy issues, potential ethical concerns, and occasional misinterpretation of data. LLMs can enhance human performance in deal situations but cannot replace the necessity for human oversight.