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Private AI for M&A diligence documents

An operations reviewer examining a plant before an acquisition compares its equipment list with the maintenance log. They need seller questions that let a colleague open the relevant documents immediately. A private AI assistant could help prepare those questions from the document set shared with the reviewer.

Author

Syntalith

Published Updated 4 min read

Asking for machine M7's maintenance records

Suppose the reviewer has access to the equipment list and maintenance log for one plant. They cannot access the HR folder. The equipment list includes machine M7, but no entry for it has been found in the supplied maintenance log.

The assistant could draft this question: “Please provide maintenance records for machine M7, which appears in the equipment list. No entry for this machine was found in the maintenance log provided.” Links beside the question lead to the list and the log reviewed. The specialist checks the basis, adjusts the wording if needed, and passes it to the person managing seller questions.

The next team member can see why the request was raised without searching both documents again. A missing entry does not establish that the machine was never maintained. The documentation may need supplementing, and the seller's response will help clarify the issue.

Keeping questions connected to the review

A working list connects questions with the passages compared and identifies the materials covered by the review. It also helps the team return to an issue when new documents arrive. If the seller supplies M7's maintenance records, the reviewer can open them beside the earlier question and assess whether they address the request. Keeping track of the document set behind a question helps a later reader understand why it was raised.

The initial scope can cover only the operational documents for one plant. That is enough to assess the help without combining the entire transaction archive. The team can judge whether questions are easier to prepare and whether references reduce repeated reading. For a small collection, a shared question list with manually added links may be sufficient.

Check the existing data room's capabilities

A virtual data room, or VDR, is an environment where transaction documents are shared with designated people. Before building a separate application, check whether the current platform already offers a useful AI integration.

Ideals describes integrations with AI tools where access follows the user's current permissions. Feature eligibility and setup are required, and subscriptions to external tools are separate. This is a hosted option to compare where the team is permitted to use it. It is not a local installation.

A separate private workspace is worth considering when the company has an agreed processing environment or wants to bring question drafting, review, and handoff into one view. The person taking over a question can then receive its supporting material with access to the sources within their agreed scope.

What enters the private workspace

Being able to read a document in the data room does not establish permission to copy it into another application. Before exporting and importing, the team needs to confirm which materials can be placed there and who may access them. In this example, the workspace contains agreed copies of operational documents; the HR folder remains outside the reviewer's work.

If the company chooses a local model, processing takes place on a designated computer or server. Ollama documents a local-only mode that disables cloud models and web search. That is a feature of the software running the model. It neither expands access to transaction documents nor defines permissions across the application.

Questions also contain information drawn from documents. Passing a list to the next reviewer therefore involves the questions themselves, their links, and retained source copies. The review coordinator needs to know who will see that material and where it will remain after the work ends. Our article on local AI for confidential client projects discusses storage choices and access for the next person in more detail.

An application for document review and seller questions

Syntalith builds AI applications for documents. For a team reviewing transaction materials, we propose a working view that brings questions together with their sources and lets staff check them before handoff. The initial work covers a selected, authorized document set and agreement on the environment in which the team can use it.

A specialist familiar with the operational records assesses the substance of the questions. The review coordinator identifies who checks them and where they go after approval. Comparing this approach with the current process and available VDR features will help determine whether a separate application merits further development. Connections to existing tools and ongoing support are agreed for this work.

Tell us about a seller question that required returning to several documents, and where the team currently conducts its review. Pricing information is available on our pricing page.

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