How to Build the Ultimate Data Room Agent

When finance professionals perform due diligence, M&A, and analysis on other companies, they might make use of a data room. Data rooms store sensitive and confidential documents for companies, such as financial statements, tax filings, contracts, and other critical company information.

However, the process of analyzing this large quantity of complex documents is time-consuming and error-prone. But with Stack AI, finance teams can automate data room document analysis and save hundreds of hours of manual labor. 

In the following blog, we’ll show you how to create an AI agent that analyzes data room documents in bulk. This allows you to execute M&A, due diligence, and other crucial financial analyses faster and more cost-effectively.

The Challenges of Due Diligence

The due diligence process is a comprehensive investigation and analysis performed by a buyer, investor, or other interested party before finalizing a significant transaction—most commonly a merger or acquisition. 

The goal is to assess the target company’s financial, legal, operational, and strategic position, uncover potential risks or liabilities, and confirm that the investment is sound. However, there are several challenges that finance professionals frequently encounter during the due diligence process.

Target companies sometimes provide incomplete, outdated, or poorly organized documentation. This slows down the review process and can raise red flags about internal controls or management transparency. 

Deals often run on tight timelines. Due diligence needs to be thorough, but compressing the process can lead to missed red flags. Sellers may also delay responses strategically to maintain leverage or push buyers to close faster.

Sharing sensitive financials, IP, or legal documents across multiple parties introduces risk. Without the right access controls and monitoring, unauthorized sharing can become real threats, especially in regulated environments.

In short, due diligence is as much about risk mitigation as it is about deal validation. And that’s why data rooms have become such an integral part of the process. Data rooms allow parties to share confidential information during due diligence.

What is a Data Room?

A data room is a secure, centralized platform used to store and manage sensitive documents during M&A transactions, due diligence, capital raises, audits, and other confidential financial processes. For finance professionals, especially those involved in deal execution, a well-organized virtual data room (VDR) is essential. 

Data rooms enable buyers, sellers, advisors, and legal teams to access critical financial, legal, and operational information in a controlled environment. Modern VDRs offer robust features such as permission-based access, document version control, watermarking, and detailed activity tracking—ensuring both transparency and security throughout the lifecycle of a deal.

In the context of M&A and due diligence, the data room serves as the backbone of information exchange. The data room accelerates timelines, reduces friction, and minimizes the risk of data leaks or compliance issues. 

Finance professionals rely on VDRs to facilitate thorough analysis of a target company’s financials, contracts, liabilities, and strategic positioning—all without compromising confidentiality. With the increasing complexity and speed of transactions, an effective data room isn’t just a convenience—it’s a critical tool for deal success.

Step-by-Step Walkthrough: How to Build the Ultimate Data Room Agent

In this section, we’ll share a step-by-step walkthrough on how to build the ultimate data room agent.

First, navigate to your stack AI dashboard, and select “New Project” -> “Workflow Builder”. Then from the list of templates, choose “Start from scratch.”

Navigate to the sidebar and choose the “Inputs” tab.

Select files and drag it onto the workflow builder.

Next, add Google from the LLM tab. 

Select Gemini 2.5 as your LLM. 

Now add instructions to the LLM.

You can copy and paste the full instructions below:

You are analyzing the data room of a company for an investment firm. Respond with the following data:

Financial Performance:

What are the trends in net income and profit after tax over the past five years?

What is the annual growth of loan book and total assets?

What is the current return on equity (RoE) and return on assets (RoA)

Liquidity and Capital:

What is the current liquidity ratio and comparison to regulatory benchmarks?

What is the change in equity to assets ratio over time?

What is the current capital adequacy ratios?

Risk and Portfolio Analysis:

What is the current non-performing loan (NPL) ratio and its change over the years?

What are the sectors most at risk in the loan portfolio?

What is the credit loss ratio compared to industry standards?

Market and Economic Environment:

What is the impact of macroeconomic factors like inflation and interest rates on financial performance?

What are current market conditions of issuing new bonds or notes?

Strategic Developments:

What are the new products and services introduced recently?

How is the company adapting to regulatory changes?

What are the company’s key strategic goals for the next five years?

Investment and Funding:

What are current sources of funding and cost of these funds?

How is the company managing foreign exchange risks?

What are the terms and conditions of recent investments or bond issuances?

Respond in bullet points. Try to answer as many of these questions as possible from the document. If the answer is not in the document, just respond with “N/A”. 

Next, fill in the prompt section as seen below:

Finally, connect the LLM to the input and output nodes.

Now let’s see the AI agent in action. Now click Save and Publish. Then Export the AI agent. 

Choose Batch for the UI. 

Give your AI agent a name and description.

By choosing the Batch interface, you can upload more than one document for analysis. Upload the documents from the data room all at the same time, and the AI agent will analyze them in parallel.

Now select Chat with Table.

This will allow you to query the results of the batch answers.

Ask questions about the results in the batch table.

Answer your questions about the company’s data room.

Export the answers in the format of your choice.

Overall, the AI agent will allow you to analyze investments and due diligence at scale with the power of generative AI.

Build Your Own Ultimate Data Room Agent Now

The walkthrough in this blog will help you build your own data room agent. Now you can speed up due diligence and other critical financial transactions. 

And nothing is stopping you from building your own version. Sign up for a free account with Stack AI to build this AI agent right now!